Tootfinder

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
MohammadAli Hamidi, Hadi Amirpour, Luigi Atzori, Christian Timmerer
https://arxiv.org/abs/2509.10114

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world environments. Although several methods have been proposed, general-purpose no-reference image quality assessment techniques often fail to capture face-specific degradations. Meanwhile, state-of-the-art FIQA models tend to be computationally intensive, limiting their practical applicability. We propose a lightweight and efficient method for FIQA, designe…

Die @… ist auf Mastodon. Also sie war hier. Denn sie ist auf der Flucht in die BigTech Netzwerke.
Dass sie hier kaum Resonanz bekommen haben, liegt möglicherweise an der Form der Postings: hauptsächlich waren es glatte PR Sprüche mit Sharepics. Kaum echte Interaktion. Ich wundere mich nicht, warum sie es niemals in meine Timline geschafft haben.
Ich w…

UNO-Flüchtlingshilfe (@unoflucht@bonn.social)
Attached: 1 image Wir verabschieden uns von diesem Kanal – aber nicht von Euch! Unser Account bleibt bestehen, damit Informationen weiterhin zugänglich sind. Neue Posts gibt es künftig auf unseren anderen Plattformen. Bleibt mit uns verbunden 👇

SynthID-Image: Image watermarking at internet scale
Sven Gowal, Rudy Bunel, Florian Stimberg, David Stutz, Guillermo Ortiz-Jimenez, Christina Kouridi, Mel Vecerik, Jamie Hayes, Sylvestre-Alvise Rebuffi, Paul Bernard, Chris Gamble, Mikl\'os Z. Horv\'ath, Fabian Kaczmarczyck, Alex Kaskasoli, Aleksandar Petrov, Ilia Shumailov, Meghana Thotakuri, Olivia Wiles, Jessica Yung, Zahra Ahmed, Victor Martin, Simon Rosen, Christopher Sav\v{c}ak, Armin Senoner, Nidhi Vyas, Pushmeet Kohli

SynthID-Image: Image watermarking at internet scale
We introduce SynthID-Image, a deep learning-based system for invisibly watermarking AI-generated imagery. This paper documents the technical desiderata, threat models, and practical challenges of deploying such a system at internet scale, addressing key requirements of effectiveness, fidelity, robustness, and security. SynthID-Image has been used to watermark over ten billion images and video frames across Google's services and its corresponding verification service is available to trusted test…

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
F\'elix Vandervorst, Bruno Deprez, Wouter Verbeke, Tim Verdonck
https://arxiv.org/abs/2510.05676

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud is a prime use case, since false claims are often the result of organised criminals that stage accidents or the same persons filing erroneous claims on multiple policies. One challenge is that graph-based approaches struggle to find meaningful representations of the data because of the high class imbalance present in fraud data. Another is that insu…

#Tesla hat in #Schweden im Tarifkonflikt mit der IF Metall immer noch mit Solidaritätblockaden zu kämpfen. Tesla wird seit Oktober 2023 bestreikt. Die zuständige Aufsichtsbehörde lehnt es ab, den Anschluss von Ladesäulen anzuordnen, da das Streikrecht durch die Verfassung geschützt ist.

Tesla får nobben – får inga nya laddstolpar
Elbilsjätten har begärt att Elmarknadsinspektionen (Ei) tar elnätsbolaget i örat, men får nobben av myndigheten.

MMSearch-Plus: A Simple Yet Challenging Benchmark for Multimodal Browsing Agents
Xijia Tao, Yihua Teng, Xinxing Su, Xinyu Fu, Jihao Wu, Chaofan Tao, Ziru Liu, Haoli Bai, Rui Liu, Lingpeng Kong
https://arxiv.org/abs/2508.21475

MMSearch-Plus: A Simple Yet Challenging Benchmark for Multimodal Browsing Agents
Large multimodal language models (MLLMs) are increasingly deployed as web agents, yet many multimodal browsing benchmarks can be solved by shallow, fixed workflows that lean on high-recall image search and nearby text-masking the genuinely multimodal challenges of fine-grained visual reasoning, provenance verification, and long-horizon tool use. We introduce MMSearch-Plus, a benchmark of 311 tasks that highly demand multimodal understanding while preserving the difficulty profile of strong text…

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Inzamamul Alam, Md Tanvir Islam, Khan Muhammad, Simon S. Woo
https://arxiv.org/abs/2510.07302 https://

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spa…

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Th\'eophane Vallaeys, Jakob Verbeek, Matthieu Cord
https://arxiv.org/abs/2510.04961 https://

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing data dimension and redundancy. Most current tokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses. Diffusion decoders have been proposed as a more principled alternative to model the distribution over images conditioned on the latent. However, matching the performance o…

Mini-Batch Robustness Verification of Deep Neural Networks
Saar Tzour-Shaday, Dana Drachsler Cohen
https://arxiv.org/abs/2508.15454 https://arxiv.org/pdf/2…

Mini-Batch Robustness Verification of Deep Neural Networks
Neural network image classifiers are ubiquitous in many safety-critical applications. However, they are susceptible to adversarial attacks. To understand their robustness to attacks, many local robustness verifiers have been proposed to analyze $ε$-balls of inputs. Yet, existing verifiers introduce a long analysis time or lose too much precision, making them less effective for a large set of inputs. In this work, we propose a new approach to local robustness: group local robustness verificatio…

Predictive Quality Assessment for Mobile Secure Graphics
Cas Steigstra, Sergey Milyaev, Shaodi You
https://arxiv.org/abs/2509.20028 https://arxiv.org/pdf/2…

Predictive Quality Assessment for Mobile Secure Graphics
The reliability of secure graphic verification, a key anti-counterfeiting tool, is undermined by poor image acquisition on smartphones. Uncontrolled user captures of these high-entropy patterns cause high false rejection rates, creating a significant 'reliability gap'. To bridge this gap, we depart from traditional perceptual IQA and introduce a framework that predictively estimates a frame's utility for the downstream verification task. We propose a lightweight model to predict a quality score…