Tootfinder

»Scams, wohin man schaut–Meta ist untätig bei Deepfake-Betrug, sagt die eigene Ethikkommission:
Betrüger können auf Facebook und Instagram ungehindert Prominente imitieren, während Meta tatenlos zuschaut und die Werbedollars einstreift«
Ach was, das ist nun so zu sagen offiziell und denen kümmert es einen Quatsch ausser Ausreden? So wie immer und seit je her sind die Profit gierig.
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Meta ist untätig bei Deepfake-Betrug, sagt die eigene Ethikkommission
Betrüger können auf Facebook und Instagram ungehindert Prominente imitieren, während Meta tatenlos zuschaut und die Werbedollars einstreift

This https://arxiv.org/abs/2505.19644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution
A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model (AM), vocoder model (VM), or other generation-specific parameters. However, progress is limited by the lack of a dedicated, systematically curated dataset. To address this, we introduce STOPA, a systematically varied and metadata-rich dataset for deepfake speech source tracing, covering 8 AMs, 6 VMs, and diverse paramete…

A probe found hundreds of ads for "nudify" apps on Meta platforms; Meta removed the ads, and says those behind the apps constantly evolve to avoid detection (Emmet Lyons/CBS News)
https://www.cbsnews.com/news/meta-instagra

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…

This https://arxiv.org/abs/2505.16512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
In recent years, the rapid development of deepfake technology has given rise to an emerging and serious threat to public security: diffusion model-based digital human generation. Unlike traditional face manipulation methods, such models can generate highly realistic videos with consistency through multimodal control signals. Their flexibility and covertness pose severe challenges to existing detection strategies. To bridge this gap, we introduce DigiFakeAV, the first large-scale multimodal digi…

Google's New AI Tool Generates Convincing Deepfakes of Riots, Conflict, and Election Fraud (Time)
https://time.com/7290050/veo-3-google-misinformation-deepfake/
http://www.memeorandum.com/250603/p107#a250603p107

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Swarup Ranjan Behera, Priyabrata Mallick, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.03364

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
In this work, we introduce the task of singing voice deepfake source attribution (SVDSA). We hypothesize that multimodal foundation models (MMFMs) such as ImageBind, LanguageBind will be most effective for SVDSA as they are better equipped for capturing subtle source-specific characteristics-such as unique timbre, pitch manipulation, or synthesis artifacts of each singing voice deepfake source due to their cross-modality pre-training. Our experiments with MMFMs, speech foundation models and mus…

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.

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.

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Petr Grinberg, Ankur Kumar, Surya Koppisetti, Gaurav Bharaj
https://arxiv.org/abs/2506.03425

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Evaluating explainability techniques, such as SHAP and LRP, in the context of audio deepfake detection is challenging due to lack of clear ground truth annotations. In the cases when we are able to obtain the ground truth, we find that these methods struggle to provide accurate explanations. In this work, we propose a novel data-driven approach to identify artifact regions in deepfake audio. We consider paired real and vocoded audio, and use the difference in time-frequency representation as th…

Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
Parul Gupta, Shreya Ghosh, Tom Gedeon, Thanh-Toan Do, Abhinav Dhall
https://arxiv.org/abs/2506.00868

Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
The rapid advancement of GenAI technology over the past few years has significantly contributed towards highly realistic deepfake content generation. Despite ongoing efforts, the research community still lacks a large-scale and reasoning capability driven deepfake benchmark dataset specifically tailored for person-centric object, context and scene manipulations. In this paper, we address this gap by introducing MultiFakeVerse, a large scale person-centric deepfake dataset, comprising 845,286 im…

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/

XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Ioan-Paul Ciobanu, Andrei-Iulian Hiji, Nicolae-Catalin Ristea, Paul Irofti, Cristian Rusu, Radu Tudor Ionescu
https://arxiv.org/abs/2506.00462

XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Recent advances in audio generation led to an increasing number of deepfakes, making the general public more vulnerable to financial scams, identity theft, and misinformation. Audio deepfake detectors promise to alleviate this issue, with many recent studies reporting accuracy rates close to 99%. However, these methods are typically tested in an in-domain setup, where the deepfake samples from the training and test sets are produced by the same generative models. To this end, we introduce XMAD-…

One small step in the fight against nonconsensual porn. #deepfake #TakeItDown

"Civitai Ban of Real People Content Deals Major Blow to the Nonconsensual AI Porn Ecosystem"

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Ruibo Fu, Xiaopeng Wang, Zhengqi Wen, Jianhua Tao, Yuankun Xie, Zhiyong Wang, Chunyu Qiang, Xuefei Liu, Cunhang Fan, Chenxing Li, Guanjun Li
https://arxiv.org/abs/2506.00375

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attack patterns. This limitation mainly arises because the models rely heavily on the distribution of the training data and fail to learn a decision boundary that captures the essential characteristics of forgeries. Additionally, relying solely on a classification loss makes it difficult to capture the intrinsic difference…

Merci ans Fotomuseum Winterthur fürs Vertrauen und die unterdessen 6 Millionen Views (12 Millionen Eyeballs)
https://www.blick.ch/digital/ki-video-mit-trump-deepfake-geht-viral-die-schweiz-existiert-nicht-id20846382.ht…

This https://arxiv.org/abs/2504.12423 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios
Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios. To address this challenge, we developed a rigorous benchmark to evaluate the performance of the ADD system under such scenarios. We introduced ADD-C, a new test dataset to evaluate the robustness of ADD systems under diverse communication conditions, inclu…

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…

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Neta Glazer, David Chernin, Idan Achituve, Sharon Gannot, Ethan Fetaya
https://arxiv.org/abs/2505.23619

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS systems continue to evolve, detection models must be able to efficiently adapt to previously unseen generation models with minimal data. This paper introduces ADD-GP, a few-shot adaptive framework based on a Gaussian Process (GP) classifier for Audio Deepfake Detection (ADD). We show how the combination of a powerful deep e…

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,…

PartialEdit: Identifying Partial Deepfakes in the Era of Neural Speech Editing
You Zhang, Baotong Tian, Lin Zhang, Zhiyao Duan
https://arxiv.org/abs/2506.02958

PartialEdit: Identifying Partial Deepfakes in the Era of Neural Speech Editing
Neural speech editing enables seamless partial edits to speech utterances, allowing modifications to selected content while preserving the rest of the audio unchanged. This useful technique, however, also poses new risks of deepfakes. To encourage research on detecting such partially edited deepfake speech, we introduce PartialEdit, a deepfake speech dataset curated using advanced neural editing techniques. We explore both detection and localization tasks on PartialEdit. Our experiments reveal …

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…