arXiv Machine Learning June 18, 2026 By Dat Nguyen, Cosmin Radoi, Romain Hermary, Marcella Astrid, Nesryne Mejri, Enjie Ghorbel, Djamila Aouada

When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift

Read the original on arXiv Machine Learning →

arXiv:2606. 19184v1 Announce Type: cross Abstract: Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including financial fraud and non-consensual explicit content.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 8

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 30

$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.

By Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida, Sebastiano Battiato

diffusion benchmarks safety

arXiv AI

Jul 2

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling

arXiv:2507. 14706v2 Announce Type: replace-cross Abstract: Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fraud from legitimate activity.

By Claudio Giusti, Luca Guarnera, Mirko Casu, Sebastiano Battiato

diffusion benchmarks safety

arXiv AI

Jul 7

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.

By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan

llms fine-tuning benchmarks

arXiv AI

Jun 29

Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

arXiv:2411. 19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content.

By Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah

multimodal benchmarks

arXiv AI

Jul 7

How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation

arXiv:2607. 03831v1 Announce Type: cross Abstract: Diffusion models have recently been repurposed for zero-shot classification, giving rise to diffusion classifiers that identify the best-matching text prompt by minimizing the noise-prediction error.

By Saba Fathi, Fardin Ayar, Maryam Abdolali, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi

llms diffusion multimodal benchmarks safety

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VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling

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How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation