The paper introduces a self-referential retrosynthesis framework for explainable AI provenance forensics that works with fixed generative models. It uses a jointly optimized encoder-decoder pair to embed client inputs, allowing the generator to produce high-fidelity outputs while enabling round-trip consistency checks. The method eliminates the need for watermarking or generator modifications and provides interpretable evidence linking generated images back to their source inputs.
arXiv:2606. 28386v1 Announce Type: cross Abstract: Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models.
By Bihe Zhao, Louis Kerner, Michel Meintz, Tameem Bakr, Franziska Boenisch, Adam Dziedzic
arXiv:2605.16080v2 Announce Type: replace
Abstract: The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image fo...
By Qing Huang, Zhipei Xu, Xuanyu Zhang, Xiangyu Yu, Jian Zhang
arXiv:2609.37783v1 Announce Type: cross
Abstract: Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not...
By Kelly McConvey, Sajad Ebrahimi, Nima Jamali, Jalehsadat Mahdavimoghaddam, Matina Mahdizadeh Sani, Maksym Taranukhin, Wentao Zhang, Jacquelyn Burkell, Yuntian Deng, Karen Eltis, Maura R. Grossman, Vered Shwartz, Ebrahim Bagheri
arXiv:2606. 26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images.
By Yangjun Wu, Keyu Yan, Yu Liu, Jingren Zhou, Fei Huang, Rong Zhang, Zhou Zhao, Fei Wu
arXiv:2608.01988v2 Announce Type: replace
Abstract: Rapid advances in image generation models call for interpretable AI-generated image detection methods that not only determine authenticity but also...
By Kun Guo, Yuzhou Yang, Haoyue Wang, Qichao Ying, Sheng Li, Zhenxing Qian
arXiv:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
By Xuechao Zou, Shun Zhang, Kai Li, Yi Zhou, Xinyu Sun, Yuhui Chen, Zhe Wu, Congyan Lang, Junliang Xing
arXiv:2607. 28955v1 Announce Type: cross Abstract: AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses growing threats to cybersecurity and social governance.
By Renxi Cheng, Chaolei Han, Jie Gui, Hongsong Wang
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization proposes a new framework that improves deepfake detection and interpretability. The approach introduces Feature-robust Augmentation—diversified degradation-aware strategies combined with supervised contrastive learning and a mean-teacher architecture—to maintain accuracy on low-quality images. For explanations, it employs evidence-grounded preference optimization, guiding the model to focus on genuine manipulation traces by learning from chosen-rejected explanation pairs that omit evidence or inject irrelevant details. The method achieved first place in the ACM Multimedia 2026 Explainable Deepfake Detection Challenge and is publicly available on GitHub.
By Zhu Xu, Jiaqi Tang, Pokai Chen, Yuxin Peng, Yang Liu
Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to evaluate. Surveillance frames, das...
The MSU team presents a modular approach to the Explainable Deepfake Detection Challenge, combining multiple DINOv3 backbones with Mesorch manipulation-localization features for real/fake classification. They incorporate a Grounding-DINO-based pseudo-mask pipeline to generate artifact evidence maps and a local contrastive objective to separate artifact from authenticity cues. For explanations, class-conditional Qwen3-VL models produce complex descriptions, which are then simplified by a GRPO-optimized text model, achieving high detection and explanation scores on the XPlainVerse dataset.
By Artem Filippov, Aleksandr Gushchin, Kirill Koltsov, Dmitriy Vatolin, Anastasia Antsiferova
The paper evaluates twenty deepfake detectors against ten recent generators, finding that accuracy has dropped from 99.5% to 76% and that adversarial perturbations can reduce detector performance to below 2%. It proposes a new detection paradigm that certifies authenticity only when a faithful reconstruction by a known generator is impossible, and shows that this calibrated detector can limit false certifications to at most 1% while remaining robust to bounded‑perturbation attacks. The study also highlights the erosion of post‑hoc verifiability, noting that newer generators can reproduce a larger fraction of previously unreplicable images.
By Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam, Samuele Poppi, Nils Lukas