Tootfinder

AOC Roasts Chris Cuomo for Believing Obvious 'Deepfake' of Her Trashing Sydney Sweeney (Eboni Boykin-Patterson/The Daily Beast)
https://www.thedailybeast.com/aoc-roasts-chris-cuomo-for-believing-obvious-deepfake-of-her-talking-sydney-sweeney/
http://www.memeorandum.com/250807/p3#a250807p3

Revisiting Simple Baselines for In-The-Wild Deepfake Detection
Orlando Castaneda, Kevin So-Tang, Kshitij Gurung
https://arxiv.org/abs/2509.04150 https://ar…

Revisiting Simple Baselines for In-The-Wild Deepfake Detection
The widespread adoption of synthetic media demands accessible deepfake detectors and realistic benchmarks. While most existing research evaluates deepfake detectors on highly controlled datasets, we focus on the recently released "in-the-wild" benchmark, Deepfake-Eval-2024. Initial reporting on Deepfake-Eval-2024 showed that three finetuned open-source models achieve accuracies between 61% and 69%, significantly lagging behind the leading commercial deepfake detector with 82% accuracy. Our work…

One of the country’s strictest bans on election deepfakes was defeated by a challenge from Elon Musk
https://www.politico.com/news/2025/08/05/elon-musk-x-court-win-california-deepfake-law-00494936

Microsoft bringt mit DragonV2.1Neural Stimmenfälschung auf Expertenniveau: Fürs perfekte Deepfake reicht nun ein halbes „Hallo, hier bin ich“. Aber keine Sorge, alles sicher – laut Nutzungsbedingungen und unhörbarem Wasserzeichen! Da kann ja nichts schiefgehen, oder? @…

ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Ting Dang
https://arxiv.org/abs/2508.04529 https://…

ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
Recent advances in audio generation systems have enabled the creation of highly realistic and immersive soundscapes, which are increasingly used in film and virtual reality. However, these audio generators also raise concerns about potential misuse, such as generating deceptive audio content for fake videos and spreading misleading information. Existing datasets for environmental sound deepfake detection (ESDD) are limited in scale and audio types. To address this gap, we have proposed EnvSDD, …

The "spicy" option on Grok's new generative AI video tool Imagine produces nude deepfakes of celebrities like Taylor Swift, even without explicit user prompting (Jess Weatherbed/The Verge)
https://www.theverge.com/report/718975/xai-grok-imag…

Grok’s ‘spicy’ video setting instantly made me Taylor Swift nude deepfakes
Groke Imagine’s “spicy” setting will generate uncensored topless videos of Taylor Swift without specifically asking the bot to take her clothes off.

NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation
Huhong Xian, Rui Liu, Berrak Sisman, Haizhou Li
https://arxiv.org/abs/2509.03829

NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation
Different from traditional sentence-level audio deepfake detection (ADD), partial audio deepfake detection (PADD) requires frame-level positioning of the location of fake speech. While some progress has been made in this area, leveraging semantic information from audio, especially named entities, remains an underexplored aspect. To this end, we propose NE-PADD, a novel method for Partial Audio Deepfake Detection (PADD) that leverages named entity knowledge through two parallel branches: Speech …

Fair Deepfake Detectors Can Generalize
Harry Cheng, Ming-Hui Liu, Yangyang Guo, Tianyi Wang, Liqiang Nie, Mohan Kankanhalli
https://arxiv.org/abs/2507.02645

Fair Deepfake Detectors Can Generalize
Deepfake detection models face two critical challenges: generalization to unseen manipulations and demographic fairness among population groups. However, existing approaches often demonstrate that these two objectives are inherently conflicting, revealing a trade-off between them. In this paper, we, for the first time, uncover and formally define a causal relationship between fairness and generalization. Building on the back-door adjustment, we show that controlling for confounders (data distri…

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Yingfan Zhou, Ester Chen, Manasa Pisipati, Aiping Xiong, Sarah Rajtmajer
https://arxiv.org/abs/2508.01906

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Synthetic images, audio, and video can now be generated and edited by Artificial Intelligence (AI). In particular, the malicious use of synthetic data has raised concerns about potential harms to cybersecurity, personal privacy, and public trust. Although AI-based detection tools exist to help identify synthetic content, their limitations often lead to user mistrust and confusion between real and fake content. This study examines the role of AI performance in influencing human trust and decisio…

Deepfakes in Criminal Investigations: Interdisciplinary Research Directions for CMC Research
Lorenz Meinen, Astrid Schom\"acker, Stefanie Wiedemann, Markus Hartmann, Timo Speith, Lena K\"astner, Niklas K\"uhl, Christian R\"uckert
https://arxiv.org/abs/2507.03457

Deepfakes in Criminal Investigations: Interdisciplinary Research Directions for CMC Research
The emergence of deepfake technologies offers both opportunities and significant challenges. While commonly associated with deception, misinformation, and fraud, deepfakes may also enable novel applications in high-stakes contexts such as criminal investigations. However, these applications raise complex technological, ethical, and legal questions. We adopt an interdisciplinary approach, drawing on computer science, philosophy, and law, to examine what it takes to responsibly use deepfakes in c…

A federal judge struck down a California law blocking large platforms from hosting deceptive AI-generated content related to elections, citing Section 230 (Chase DiFeliciantonio/Politico)
https://www.politico.com/news/2025/08/05/elon-musk-x-cou…

A whistleblower details Clothoff, an AI-powered nudify app that owns 10 similar services; Clothoff had 27M visitors in H1 2024 and produced 200K images daily (Spiegel Online)
https://www.spiegel.de/internationa…

Using AI to Humiliate Women: The Men Behind Deepfake Pornography
AI-generated naked images of real women is the business model behind Clothoff, a dubious "nudify" app that has millions of visitors. Now, a whistleblower has provided details of just how cynical the site's operators are.

Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework
Andrea Di Pierno (IMT School of Advanced Studies), Luca Guarnera (University of Catania), Dario Allegra (University of Catania), Sebastiano Battiato (University of Catania)
https://arxiv.org/abs/2508.02521

Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework
The proliferation of audio deepfakes poses a growing threat to trust in digital communications. While detection methods have advanced, attributing audio deepfakes to their source models remains an underexplored yet crucial challenge. In this paper we introduce LAVA (Layered Architecture for Voice Attribution), a hierarchical framework for audio deepfake detection and model recognition that leverages attention-enhanced latent representations extracted by a convolutional autoencoder trained solel…

Researchers at security giant CrowdStrike say they have seen hundreds of cases where
North Koreans posing as remote IT workers have infiltrated companies to generate money for the regime,
marking a sharp increase over previous years.
Per CrowdStrike’s latest threat-hunting report,
the company has identified more than 320 incidents over the past 12 months,
up by 220% from the year earlier,
in which North Koreans gained fraudulent employment at Western compa…

North Korean spies posing as remote workers have infiltrated hundreds of companies, says CrowdStrike | TechCrunch
North Korean IT workers are increasingly using generative AI to draft resumes and "deepfake" their appearances to make money for North Korea's sanctioned nuclear weapons program.

Try it. AI or "smart autocomplete" is getting better...
"A.I. Videos Have Never Been Better. Can You Tell What’s Real?"
https://www.nytimes.com/interactive/2025/0

A.I. Videos Have Never Been Better. Can You Tell What’s Real?
In this quiz, you can guess whether these videos were made by A.I.

Multi-Granularity Adaptive Time-Frequency Attention Framework for Audio Deepfake Detection under Real-World Communication Degradations
Haohan Shi, Xiyu Shi, Safak Dogan, Tianjin Huang, Yunxiao Zhang
https://arxiv.org/abs/2508.01467

Multi-Granularity Adaptive Time-Frequency Attention Framework for Audio Deepfake Detection under Real-World Communication Degradations
The rise of highly convincing synthetic speech poses a growing threat to audio communications. Although existing Audio Deepfake Detection (ADD) methods have demonstrated good performance under clean conditions, their effectiveness drops significantly under degradations such as packet losses and speech codec compression in real-world communication environments. In this work, we propose the first unified framework for robust ADD under such degradations, which is designed to effectively accommodat…

Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models
Sandipana Dowerah, Atharva Kulkarni, Ajinkya Kulkarni, Hoan My Tran, Joonas Kalda, Artem Fedorchenko, Benoit Fauve, Damien Lolive, Tanel Alum\"ae, Matthew Magimai Doss
https://arxiv.org/abs/2509.02859

Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models
Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocol…

A Deepfake Nightmare: Stalker Allegedly Made Sexual AI Images of Ex-Girlfriends and Their Families https://www.404media.co/deepfake-harassment-ohio-undress-clothoff-nudify-apps/

HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Xuecheng Wu, Danlei Huang, Heli Sun, Xinyi Yin, Yifan Wang, Hao Wang, Jia Zhang, Fei Wang, Peihao Guo, Suyu Xing, Junxiao Xue, Liang He
https://arxiv.org/abs/2507.22781

HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Advances in Generative AI have made video-level deepfake detection increasingly challenging, exposing the limitations of current detection techniques. In this paper, we present HOLA, our solution to the Video-Level Deepfake Detection track of 2025 1M-Deepfakes Detection Challenge. Inspired by the success of large-scale pre-training in the general domain, we first scale audio-visual self-supervised pre-training in the multimodal video-level deepfake detection, which leverages our self-built data…

A federal judge struck down a California law blocking large platforms from hosting deceptive AI-generated content related to elections, citing Section 230 (Chase DiFeliciantonio/Politico)
https://www.politico.com/news/2025/08/05/elon-musk-x-cou…

Wir leben in einer Gesellschaft, in der eine Privatfirma keine Knöllchen an Falschparker verteilen darf, weil das eine originäre hoheitliche Aufgabe des staatlichen gewaltmonopols ist, der der staat aber kaum nachkommt, wo aber US-Oligarchen Urheberrechtsvetletzungen, Morddrohungen und Vergewaltigungsankündigungen in kommerziellen social media sites regulieren sollen, weil das dem staat und seinem gewaltmonopol sowas von egal ist, es sei denn es handelt sich um ein deepfake des kanzlers in dem …

Wir leben in einer Gesellschaft, in der eine Privatfirma keine Knöllchen an Falschparker verteilen darf, weil das eine originäre hoheitliche Aufgabe des staatlichen gewaltmonopols ist, der der staat aber kaum nachkommt, wo aber US-Oligarchen Urheberrechtsvetletzungen, Morddrohungen und Vergewaltigungsankündigungen in kommerziellen social media sites regulieren sollen, weil das dem staat und seinem gewaltmonopol sowas von egal ist, es sei denn es handelt sich um ein deepfake des kanzlers in dem …

#SteadyCommunityContent #KIMissbrauch #Retröt
Wie gut erkennt man gefälschte Stimmen am Telefon?
Eine neue Studie beschäftigt sich damit, inwiefern Menschen Sprach-

Deepfake-Betrug: Wie gut erkennen Menschen Sprach-Deepfakes?
Können Menschen Sprach-Deepfakes erkennen? Eine neue Studie zeigt ernüchternde Ergebnisse und macht deutlich, dass automatische Erkennungssysteme notwendig sind.

Addressing Deepfake Issue in Selfie banking through camera based authentication
Subhrojyoti Mukherjee, Manoranjan Mohanty
https://arxiv.org/abs/2508.19714 https://

Addressing Deepfake Issue in Selfie banking through camera based authentication
Fake images in selfie banking are increasingly becoming a threat. Previously, it was just Photoshop, but now deep learning technologies enable us to create highly realistic fake identities, which fraudsters exploit to bypass biometric systems such as facial recognition in online banking. This paper explores the use of an already established forensic recognition system, previously used for picture camera localization, in deepfake detection.

Wav2DF-TSL: Two-stage Learning with Efficient Pre-training and Hierarchical Experts Fusion for Robust Audio Deepfake Detection
Yunqi Hao, Yihao Chen, Minqiang Xu, Jianbo Zhan, Liang He, Lei Fang, Sian Fang, Lin Liu
https://arxiv.org/abs/2509.04161

Wav2DF-TSL: Two-stage Learning with Efficient Pre-training and Hierarchical Experts Fusion for Robust Audio Deepfake Detection
In recent years, self-supervised learning (SSL) models have made significant progress in audio deepfake detection (ADD) tasks. However, existing SSL models mainly rely on large-scale real speech for pre-training and lack the learning of spoofed samples, which leads to susceptibility to domain bias during the fine-tuning process of the ADD task. To this end, we propose a two-stage learning strategy (Wav2DF-TSL) based on pre-training and hierarchical expert fusion for robust audio deepfake detect…

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark
Xi Xuan, Yang Xiao, Rohan Kumar Das, Tomi Kinnunen
https://arxiv.org/abs/2508.04143 https://

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark
Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages. Current research has largely focused on detecting fake speech, but little attention has been given to tracing the source models used to generate it. This paper introduces the first benchmark for multili…

Generalizable Audio Deepfake Detection via Hierarchical Structure Learning and Feature Whitening in Poincar\'e sphere
Mingru Yang, Yanmei Gu, Qianhua He, Yanxiong Li, Peirong Zhang, Yongqiang Chen, Zhiming Wang, Huijia Zhu, Jian Liu, Weiqiang Wang
https://arxiv.org/abs/2508.01897

Generalizable Audio Deepfake Detection via Hierarchical Structure Learning and Feature Whitening in Poincaré sphere
Audio deepfake detection (ADD) faces critical generalization challenges due to diverse real-world spoofing attacks and domain variations. However, existing methods primarily rely on Euclidean distances, failing to adequately capture the intrinsic hierarchical structures associated with attack categories and domain factors. To address these issues, we design a novel framework Poin-HierNet to construct domain-invariant hierarchical representations in the Poincaré sphere. Poin-HierNet includes th…

Watching the movie "G20" which came out a few months ago. The plot involves Bitcoin maxis taking world leaders hostage in order to take voice samples and deepfake social media videos to destabilise currencies (world leaders being notoriously difficult to get voice samples of).
Anyway, when the bad guy uploads the faked videos to social media (Instagram, VK, Facebook) you see that the list doesn't include Twitter/X, but does include Orkut - a network that was switched off …

Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
H M Mohaimanul Islam, Huynh Q. N. Vo, Aditya Rane
https://arxiv.org/abs/2507.17010 https:/…

Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational c…

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds
Qizhou Wang, Hanxun Huang, Guansong Pang, Sarah Erfani, Christopher Leckie
https://arxiv.org/abs/2509.04345

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds
Speech generation systems can produce remarkably realistic vocalisations that are often indistinguishable from human speech, posing significant authenticity challenges. Although numerous deepfake detection methods have been developed, their effectiveness in real-world environments remains unrealiable due to the domain shift between training and test samples arising from diverse human speech and fast evolving speech synthesis systems. This is not adequately addressed by current datasets, which l…

Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
Xiaoya Zhu, Yibing Nan, Shiguo Lian
https://arxiv.org/abs/2508.11464 https://…

Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
With the rapid development of technology in the field of AI, deepfake technology has emerged as a double-edged sword. It has not only created a large amount of AI-generated content but also posed unprecedented challenges to digital security. The task of the competition is to determine whether a face image is a Deepfake image and output its probability score of being a Deepfake image. In the image track competition, our approach is based on the Swin Transformer V2-B classification network. And o…

"Interviewees should take note and always record their own version of a conversation in any hostile forum, although of course they could be accused of faking the real version!"
#DeepFake
#GenAI

How To Keep The Truth Alive
In the age of deepfakes and brazen lies, we need to figure out who we can trust. Credible media institutions are more crucial than ever.

Robust Localization of Partially Fake Speech: Metrics, Models, and Out-of-Domain Evaluation
Hieu-Thi Luong, Inbal Rimons, Haim Permuter, Kong Aik Lee, Eng Siong Chng
https://arxiv.org/abs/2507.03468

Robust Localization of Partially Fake Speech: Metrics, Models, and Out-of-Domain Evaluation
Partial audio deepfake localization pose unique challenges and remain underexplored compared to full-utterance spoofing detection. While recent methods report strong in-domain performance, their real-world utility remains unclear. In this analysis, we critically examine the limitations of current evaluation practices, particularly the widespread use of Equal Error Rate (EER), which often obscures generalization and deployment readiness. We propose reframing the localization task as a sequential…

LayLens: Improving Deepfake Understanding through Simplified Explanations
Abhijeet Narang, Parul Gupta, Liuyijia Su, Abhinav Dhall
https://arxiv.org/abs/2507.10066

LayLens: Improving Deepfake Understanding through Simplified Explanations
This demonstration paper presents $\mathbf{LayLens}$, a tool aimed to make deepfake understanding easier for users of all educational backgrounds. While prior works often rely on outputs containing technical jargon, LayLens bridges the gap between model reasoning and human understanding through a three-stage pipeline: (1) explainable deepfake detection using a state-of-the-art forgery localization model, (2) natural language simplification of technical explanations using a vision-language model…

The A.O.C. Deepfake Was Terrible. The Proposed Solution Is Delusional. (Zeynep Tufekci/New York Times)
https://www.nytimes.com/2025/08/11/opinion/alexandria-ocasio-cortez-deepfake-ai.html?unlocked_article_code=1.dU8.c_eL.5W-UVl551e0G&smid=url-share
http://www.memeorandum.com/250811/p40#a250811p40

Multi-level SSL Feature Gating for Audio Deepfake Detection
Hoan My Tran, Damien Lolive, Aghilas Sini, Arnaud Delhay, Pierre-Fran\c{c}ois Marteau, David Guennec
https://arxiv.org/abs/2509.03409

Multi-level SSL Feature Gating for Audio Deepfake Detection
Recent advancements in generative AI, particularly in speech synthesis, have enabled the generation of highly natural-sounding synthetic speech that closely mimics human voices. While these innovations hold promise for applications like assistive technologies, they also pose significant risks, including misuse for fraudulent activities, identity theft, and security threats. Current research on spoofing detection countermeasures remains limited by generalization to unseen deepfake attacks and la…

Intergenerational Support for Deepfake Scams Targeting Older Adults
Karina LaRubbio, Alyssa Lanter, Seihyun Lee, Mahima Ramesh, Diana Freed
https://arxiv.org/abs/2508.11579 http…

Intergenerational Support for Deepfake Scams Targeting Older Adults
AI-enhanced scams now employ deepfake technology to produce convincing audio and visual impersonations of trusted family members, often grandchildren, in real time. These attacks fabricate urgent scenarios, such as legal or medical emergencies, to socially engineer older adults into transferring money. The realism of these AI-generated impersonations undermines traditional cues used to detect fraud, making them a powerful tool for financial exploitation. In this study, we explore older adults' …

"What do you expect? You're part of the internet": Analyzing Celebrities' Experiences as Usees of Deepfake Technology
John Twomey, Sarah Foley, Sarah Robinson, Michael Quayle, Matthew Peter Aylett, Conor Linehan, Gillian Murphy
https://arxiv.org/abs/2507.13065

"What do you expect? You're part of the internet": Analyzing Celebrities' Experiences as Usees of Deepfake Technology
Deepfake technology is often used to create non-consensual synthetic intimate imagery (NSII), mainly of celebrity women. Through Critical Discursive Psychological analysis we ask; i) how celebrities construct being targeted by deepfakes and ii) how they navigate infrastructural and social obstacles when seeking recourse. In this paper, we adopt Baumers concept of Usees (stakeholders who are non-consenting, unaware and directly targeted by technology), to understand public statements made by eig…

Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling
Xuanjun Chen, Shih-Peng Cheng, Jiawei Du, Lin Zhang, Xiaoxiao Miao, Chung-Che Wang, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang
https://arxiv.org/abs/2508.02000

Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling
Audio-visual temporal deepfake localization under the content-driven partial manipulation remains a highly challenging task. In this scenario, the deepfake regions are usually only spanning a few frames, with the majority of the rest remaining identical to the original. To tackle this, we propose a Hierarchical Boundary Modeling Network (HBMNet), which includes three modules: an Audio-Visual Feature Encoder that extracts discriminative frame-level representations, a Coarse Proposal Generator th…

Two things on this:
1 - To protect people from deepfakes has merit and is very, very needed. Urgently, even.
2 - Every single time people tried to use copyright law to do stuff copyright law is not intended to do, the results were catastrophic. No problems were solved that way, and new problems were created.
Stop it. Just stop.

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
Lixin Jia, Zhiqing Guo, Gaobo Yang, Liejun Wang, Keqin Li
https://arxiv.org/abs/2508.10741

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
The emergence of deepfake technology has introduced a range of societal problems, garnering considerable attention. Current deepfake detection methods perform well on specific datasets, but exhibit poor performance when applied to datasets with unknown forgery techniques. Moreover, as the gap between emerging and traditional forgery techniques continues to widen, cross-domain detection methods that rely on common forgery traces are becoming increasingly ineffective. This situation highlights th…

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges
Hashim Ali, Surya Subramani, Raksha Varahamurthy, Nithin Adupa, Lekha Bollinani, Hafiz Malik
https://arxiv.org/abs/2507.00324

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges
Recent advances in speech synthesis have introduced unprecedented challenges in maintaining voice authenticity, particularly concerning public figures who are frequent targets of impersonation attacks. This paper presents a comprehensive methodology for collecting, curating, and generating synthetic speech data for political figures and a detailed analysis of challenges encountered. We introduce a systematic approach incorporating an automated pipeline for collecting high-quality bonafide speec…

Ein Gedanke, den ich nicht mehr loswerde, wenn ich Geschichten über alte Leute höre, die Opfer von Deepfake-Betrügern wurden: da sind massenweise Menschen dazu bereit, Geld zu bezahlen, damit ihr Kind oder ihr Enkelkind nicht ins Gefängnis kommt, weil es jemanden getötet hat. What? Ja, Enkeltrickbetrüger sind scheiße, aber was sind das für Menschen, die sagen "klar zahle ich Geld dafür, dass mein Kind, das gerade eine Straftat begangen hat, dafür nicht ins Gefängnis muss"? 1/x

DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection
Marcel Klemt, Carlotta Segna, Anna Rohrbach
https://arxiv.org/abs/2506.05851 https://…

DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection
Generative AI advances rapidly, allowing the creation of very realistic manipulated video and audio. This progress presents a significant security and ethical threat, as malicious users can exploit DeepFake techniques to spread misinformation. Recent DeepFake detection approaches explore the multimodal (audio-video) threat scenario. In particular, there is a lack of reproducibility and critical issues with existing datasets - such as the recently uncovered silence shortcut in the widely used Fa…

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust
Claudiu Popa, Rex Pallath, Liam Cunningham, Hewad Tahiri, Abiram Kesavarajah, Tao Wu
https://arxiv.org/abs/2506.07363

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust
Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, a…

SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
Wen Huang, Yanmei Gu, Zhiming Wang, Huijia Zhu, Yanmin Qian
https://arxiv.org/abs/2507.21463

SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
As speech generation technology advances, the risk of misuse through deepfake audio has become a pressing concern, which underscores the critical need for robust detection systems. However, many existing speech deepfake datasets are limited in scale and diversity, making it challenging to train models that can generalize well to unseen deepfakes. To address these gaps, we introduce SpeechFake, a large-scale dataset designed specifically for speech deepfake detection. SpeechFake includes over 3 …

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu, Chuangchuang Tan, Haotong Qin, Bing Li, Yunchao Wei, Yao Zhao
https://arxiv.org/abs/2506.20548

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have become nearly indistinguishable from real ones. These images are widely shared across Online Social Networks (OSNs), raising concerns about their misuse. Existing deepfake detection methods overlook the ``block effects" introduced by compression in OSNs, which obscure deepfake artifacts, and primarily focus on raw images,…

Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System
Hashim Ali, Surya Subramani, Lekha Bollinani, Nithin Sai Adupa, Sali El-Loh, Hafiz Malik
https://arxiv.org/abs/2508.20983

Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System
The SAFE Challenge evaluates synthetic speech detection across three tasks: unmodified audio, processed audio with compression artifacts, and laundered audio designed to evade detection. We systematically explore self-supervised learning (SSL) front-ends, training data compositions, and audio length configurations for robust deepfake detection. Our AASIST-based approach incorporates WavLM large frontend with RawBoost augmentation, trained on a multilingual dataset of 256,600 samples spanning 9 …

Crosslisted article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Combating Digitally Altered Images: Deepfake Detection
Saksham Kumar, Rhythm Narang

Post-training for Deepfake Speech Detection
Wanying Ge, Xin Wang, Xuechen Liu, Junichi Yamagishi
https://arxiv.org/abs/2506.21090 https://

Post-training for Deepfake Speech Detection
We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained m…

Companies, including Google and Cisco, have reinstated in-person interviews for some roles to combat AI-driven cheating, with some using deepfake detection tech (Ray A. Smith/Wall Street Journal)
https://www.wsj.com/lifestyle/careers/ai-j

"He’s a walking, talking deepfake."
- Maureen Dowd, #NYT
$: #NY_Times #quote #paywall

Frame-level Temporal Difference Learning for Partial Deepfake Speech Detection
Menglu Li, Xiao-Ping Zhang, Lian Zhao
https://arxiv.org/abs/2507.15101 https…

Frame-level Temporal Difference Learning for Partial Deepfake Speech Detection
Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake segments. As deepfake generation techniques increasingly smooth these transitions, detection has become more challenging. To address this, our work introduces a new perspective by analyzing frame-level temp…

Manipulated Regions Localization For Partially Deepfake Audio: A Survey
Jiayi He, Jiangyan Yi, Jianhua Tao, Siding Zeng, Hao Gu
https://arxiv.org/abs/2506.14396

Manipulated Regions Localization For Partially Deepfake Audio: A Survey
With the development of audio deepfake techniques, attacks with partially deepfake audio are beginning to rise. Compared to fully deepfake, it is much harder to be identified by the detector due to the partially cryptic manipulation, resulting in higher security risks. Although some studies have been launched, there is no comprehensive review to systematically introduce the current situations and development trends for addressing this issue. Thus, in this survey, we are the first to outline a s…

A Comparative Study on Proactive and Passive Detection of Deepfake Speech
Chia-Hua Wu, Wanying Ge, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang
https://arxiv.org/abs/2506.14398

A Comparative Study on Proactive and Passive Detection of Deepfake Speech
Solutions for defending against deepfake speech fall into two categories: proactive watermarking models and passive conventional deepfake detectors. While both address common threats, their differences in training, optimization, and evaluation prevent a unified protocol for joint evaluation and selecting the best solutions for different cases. This work proposes a framework to evaluate both model types in deepfake speech detection. To ensure fair comparison and minimize discrepancies, all model…

Generalizable Detection of Audio Deepfakes
Jose A. Lopez, Georg Stemmer, H\'ector Cordourier Maruri
https://arxiv.org/abs/2507.01750 https://

Generalizable Detection of Audio Deepfakes
In this paper, we present our comprehensive study aimed at enhancing the generalization capabilities of audio deepfake detection models. We investigate the performance of various pre-trained backbones, including Wav2Vec2, WavLM, and Whisper, across a diverse set of datasets, including those from the ASVspoof challenges and additional sources. Our experiments focus on the effects of different data augmentation strategies and loss functions on model performance. The results of our research demons…

Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform
Yuankun Xie, Ruibo Fu, Xiaopeng Wang, Zhiyong Wang, Ya Li, Zhengqi Wen, Haonnan Cheng, Long Ye
https://arxiv.org/abs/2508.10559

Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform
The rapid advancement of speech generation technology has led to the widespread proliferation of deepfake speech across social media platforms. While deepfake audio countermeasures (CMs) achieve promising results on public datasets, their performance degrades significantly in cross-domain scenarios. To advance CMs for real-world deepfake detection, we first propose the Fake Speech Wild (FSW) dataset, which includes 254 hours of real and deepfake audio from four different media platforms, focusi…

ClearMask: Noise-Free and Naturalness-Preserving Protection Against Voice Deepfake Attacks
Yuanda Wang, Bocheng Chen, Hanqing Guo, Guangjing Wang, Weikang Ding, Qiben Yan
https://arxiv.org/abs/2508.17660

ClearMask: Noise-Free and Naturalness-Preserving Protection Against Voice Deepfake Attacks
Voice deepfake attacks, which artificially impersonate human speech for malicious purposes, have emerged as a severe threat. Existing defenses typically inject noise into human speech to compromise voice encoders in speech synthesis models. However, these methods degrade audio quality and require prior knowledge of the attack approaches, limiting their effectiveness in diverse scenarios. Moreover, real-time audios, such as speech in virtual meetings and voice messages, are still exposed to voic…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/2]:
- Towards Generalized Source Tracing for Codec-Based Deepfake Speech
Xuanjun Chen, I-Ming Lin, Lin Zhang, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang

Toward an African Agenda for AI Safety
Samuel T. Segun, Rachel Adams, Ana Florido, Scott Timcke, Jonathan Shock, Leah Junck, Fola Adeleke, Nicolas Grossman, Ayantola Alayande, Jerry John Kponyo, Matthew Smith, Dickson Marfo Fosu, Prince Dawson Tetteh, Juliet Arthur, Stephanie Kasaon, Odilile Ayodele, Laetitia Badolo, Paul Plantinga, Michael Gastrow, Sumaya Nur Adan, Joanna Wiaterek, Cecil Abungu, Kojo Apeagyei, Luise Eder, Tegawende Bissyande

Toward an African Agenda for AI Safety
This paper maps Africa's distinctive AI risk profile, from deepfake fuelled electoral interference and data colonial dependency to compute scarcity, labour disruption and disproportionate exposure to climate driven environmental costs. While major benefits are promised to accrue, the availability, development and adoption of AI also mean that African people and countries face particular AI safety risks, from large scale labour market disruptions to the nefarious use of AI to manipulate public o…

FakeHunter: Multimodal Step-by-Step Reasoning for Explainable Video Forensics
Chen Chen, Runze Li, Zejun Zhang, Pukun Zhao, Fanqing Zhou, Longxiang Wang, Haojian Huang
https://arxiv.org/abs/2508.14581 …

FakeHunter: Multimodal Step-by-Step Reasoning for Explainable Video Forensics
FakeHunter is a multimodal deepfake detection framework that combines memory-guided retrieval, chain-of-thought (Observation-Thought-Action) reasoning, and tool-augmented verification to provide accurate and interpretable video forensics. FakeHunter encodes visual content using CLIP and audio using CLAP, generating joint audio-visual embeddings that retrieve semantically similar real exemplars from a FAISS-indexed memory bank for contextual grounding. Guided by the retrieved context, the system…

IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection
Abhay Kumar, Kunal Verma, Omkar More
https://arxiv.org/abs/2506.19014 https://

IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection
Advancements in audio deepfake technology offers benefits like AI assistants, better accessibility for speech impairments, and enhanced entertainment. However, it also poses significant risks to security, privacy, and trust in digital communications. Detecting and mitigating these threats requires comprehensive datasets. Existing datasets lack diverse ethnic accents, making them inadequate for many real-world scenarios. Consequently, models trained on these datasets struggle to detect audio dee…

Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection
Yassine El Kheir, Arnab Das, Enes Erdem Erdogan, Fabian Ritter-Guttierez, Tim Polzehl, Sebastian M\"oller
https://arxiv.org/abs/2507.20417

Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection
Recent advances in synthetic speech have made audio deepfakes increasingly realistic, posing significant security risks. Existing detection methods that rely on a single modality, either raw waveform embeddings or spectral based features, are vulnerable to non spoof disturbances and often overfit to known forgery algorithms, resulting in poor generalization to unseen attacks. To address these shortcomings, we investigate hybrid fusion frameworks that integrate self supervised learning (SSL) bas…

From Sharpness to Better Generalization for Speech Deepfake Detection
Wen Huang, Xuechen Liu, Xin Wang, Junichi Yamagishi, Yanmin Qian
https://arxiv.org/abs/2506.11532

From Sharpness to Better Generalization for Speech Deepfake Detection
Generalization remains a critical challenge in speech deepfake detection (SDD). While various approaches aim to improve robustness, generalization is typically assessed through performance metrics like equal error rate without a theoretical framework to explain model performance. This work investigates sharpness as a theoretical proxy for generalization in SDD. We analyze how sharpness responds to domain shifts and find it increases in unseen conditions, indicating higher model sensitivity. Bas…

LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech
Xuechen Liu, Wanying Ge, Xin Wang, Junichi Yamagishi
https://arxiv.org/abs/2507.16220

LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech
This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio conditions. The generation part of the recipe outputs audios from the input dataset with several critical characteristics, such as longer duration, noisy conditions, and containing multiple speakers, in a controllable fashion. The corresponding detection and localization protocol uses models. We conduct experiments b…

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges
Zhiqiang Yang, Renshuai Tao, Xiaolong Zheng, Guodong Yang, Chunjie Zhang
https://arxiv.org/abs/2508.09022

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges
Existing deepfake detection methods heavily depend on labeled training data. However, as AI-generated content becomes increasingly realistic, even \textbf{human annotators struggle to distinguish} between deepfakes and authentic images. This makes the labeling process both time-consuming and less reliable. Specifically, there is a growing demand for approaches that can effectively utilize large-scale unlabeled data from online social networks. Unlike typical unsupervised learning tasks, where c…

Perturbed Public Voices (P$^{2}$V): A Dataset for Robust Audio Deepfake Detection
Chongyang Gao, Marco Postiglione, Isabel Gortner, Sarit Kraus, V. S. Subrahmanian
https://arxiv.org/abs/2508.10949

Perturbed Public Voices (P$^{2}$V): A Dataset for Robust Audio Deepfake Detection
Current audio deepfake detectors cannot be trusted. While they excel on controlled benchmarks, they fail when tested in the real world. We introduce Perturbed Public Voices (P$^{2}$V), an IRB-approved dataset capturing three critical aspects of malicious deepfakes: (1) identity-consistent transcripts via LLMs, (2) environmental and adversarial noise, and (3) state-of-the-art voice cloning (2020-2025). Experiments reveal alarming vulnerabilities of 22 recent audio deepfake detectors: models trai…

Open-Set Source Tracing of Audio Deepfake Systems
Nicholas Klein, Hemlata Tak, Elie Khoury
https://arxiv.org/abs/2507.06470 https://a…

Open-Set Source Tracing of Audio Deepfake Systems
Existing research on source tracing of audio deepfake systems has focused primarily on the closed-set scenario, while studies that evaluate open-set performance are limited to a small number of unseen systems. Due to the large number of emerging audio deepfake systems, robust open-set source tracing is critical. We leverage the protocol of the Interspeech 2025 special session on source tracing to evaluate methods for improving open-set source tracing performance. We introduce a novel adaptation…

Generalizable Audio Spoofing Detection using Non-Semantic Representations
Arnab Das, Yassine El Kheir, Carlos Franzreb, Tim Herzig, Tim Polzehl, Sebastian M\"oller
https://arxiv.org/abs/2509.00186

Generalizable Audio Spoofing Detection using Non-Semantic Representations
Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. …

SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Vojt\v{e}ch Stan\v{e}k, Karel Srna, Anton Firc, Kamil Malinka
https://arxiv.org/abs/2508.07944 https://…

SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Characteristics Deepfake (SCDF) dataset: a novel, richly annotated resource enabling systematic evaluation of demographic biases in deepfake speech detection. SCDF contains over 237,000 utterances in a balanced representation of both male and female speakers spanning five languages and a wide age range. We evaluate seve…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- A Survey on Speech Deepfake Detection
Menglu Li, Yasaman Ahmadiadli, Xiao-Ping Zhang
…

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Xi Xuan, Zimo Zhu, Wenxin Zhang, Yi-Cheng Lin, Tomi Kinnunen
https://arxiv.org/abs/2508.09294

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Advances in speech synthesis intensify security threats, motivating real-time deepfake detection research. We investigate whether bidirectional Mamba can serve as a competitive alternative to Self-Attention in detecting synthetic speech. Our solution, Fake-Mamba, integrates an XLSR front-end with bidirectional Mamba to capture both local and global artifacts. Our core innovation introduces three efficient encoders: TransBiMamba, ConBiMamba, and PN-BiMamba. Leveraging XLSR's rich linguistic repr…

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Kutub Uddin, Awais Khan, Muhammad Umar Farooq, Khalid Malik
https://arxiv.org/abs/2507.13170

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Audio plays a crucial role in applications like speaker verification, voice-enabled smart devices, and audio conferencing. However, audio manipulations, such as deepfakes, pose significant risks by enabling the spread of misinformation. Our empirical analysis reveals that existing methods for detecting deepfake audio are often vulnerable to anti-forensic (AF) attacks, particularly those attacked using generative adversarial networks. In this article, we propose a novel collaborative learning me…

KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features
Ivan Kukanov, Jun Wah Ng
https://arxiv.org/abs/2508.07337 https://

KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features
The rapid development of audio-driven talking head generators and advanced Text-To-Speech (TTS) models has led to more sophisticated temporal deepfakes. These advances highlight the need for robust methods capable of detecting and localizing deepfakes, even under novel, unseen attack scenarios. Current state-of-the-art deepfake detectors, while accurate, are often computationally expensive and struggle to generalize to novel manipulation techniques. To address these challenges, we propose multi…

Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Davide Salvi, Viola Negroni, Sara Mandelli, Paolo Bestagini, Stefano Tubaro
https://arxiv.org/abs/2507.08626

Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Recent advances in generative AI have made the creation of speech deepfakes widely accessible, posing serious challenges to digital trust. To counter this, various speech deepfake detection strategies have been proposed, including Person-of-Interest (POI) approaches, which focus on identifying impersonations of specific individuals by modeling and analyzing their unique vocal traits. Despite their excellent performance, the existing methods offer limited granularity and lack interpretability. I…

TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
Adriana Stan, David Combei, Dan Oneata, Hora Cucu
https://arxiv.org/abs/2506.05802

TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
Deepfake detection has gained significant attention across audio, text, and image modalities, with high accuracy in distinguishing real from fake. However, identifying the exact source--such as the system or model behind a deepfake--remains a less studied problem. In this paper, we take a significant step forward in audio deepfake model attribution or source tracing by proposing a training-free, green AI approach based entirely on k-Nearest Neighbors (kNN). Leveraging a pre-trained self-supervi…

Multimodal Zero-Shot Framework for Deepfake Hate Speech Detection in Low-Resource Languages
Rishabh Ranjan, Likhith Ayinala, Mayank Vatsa, Richa Singh
https://arxiv.org/abs/2506.08372

Multimodal Zero-Shot Framework for Deepfake Hate Speech Detection in Low-Resource Languages
This paper introduces a novel multimodal framework for hate speech detection in deepfake audio, excelling even in zero-shot scenarios. Unlike previous approaches, our method uses contrastive learning to jointly align audio and text representations across languages. We present the first benchmark dataset with 127,290 paired text and synthesized speech samples in six languages: English and five low-resource Indian languages (Hindi, Bengali, Marathi, Tamil, Telugu). Our model learns a shared seman…

Towards Scalable AASIST: Refining Graph Attention for Speech Deepfake Detection
Ivan Viakhirev, Daniil Sirota, Aleksandr Smirnov, Kirill Borodin
https://arxiv.org/abs/2507.11777

Towards Scalable AASIST: Refining Graph Attention for Speech Deepfake Detection
Advances in voice conversion and text-to-speech synthesis have made automatic speaker verification (ASV) systems more susceptible to spoofing attacks. This work explores modest refinements to the AASIST anti-spoofing architecture. It incorporates a frozen Wav2Vec 2.0 encoder to retain self-supervised speech representations in limited-data settings, substitutes the original graph attention block with a standardized multi-head attention module using heterogeneous query projections, and replaces h…

Replaced article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Generalizable speech deepfake detection via meta-learned LoRA
Janne Laakkonen, Ivan Kukanov, Ville Hautam\"aki

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Perturbed Public Voices (P$^{2}$V): A Dataset for Robust Audio Deepfake Detection
Chongyang Gao, Marco Postiglione, Isabel Gortner, Sarit Kraus, V. S. Subrahmanian

Enkidu: Universal Frequential Perturbation for Real-Time Audio Privacy Protection against Voice Deepfakes
Zhou Feng, Jiahao Chen, Chunyi Zhou, Yuwen Pu, Qingming Li, Tianyu Du, Shouling Ji
https://arxiv.org/abs/2507.12932

Enkidu: Universal Frequential Perturbation for Real-Time Audio Privacy Protection against Voice Deepfakes
The rapid advancement of voice deepfake technologies has raised serious concerns about user audio privacy, as attackers increasingly exploit publicly available voice data to generate convincing fake audio for malicious purposes such as identity theft, financial fraud, and misinformation campaigns. While existing defense methods offer partial protection, they face critical limitations, including weak adaptability to unseen user data, poor scalability to long audio, rigid reliance on white-box kn…

Replaced article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- A Survey on Speech Deepfake Detection
Menglu Li, Yasaman Ahmadiadli, Xiao-Ping Zhang

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- A Survey on Speech Deepfake Detection
Menglu Li, Yasaman Ahmadiadli, Xiao-Ping Zhang
…