arXiv:2508.01248v5 Announce Type: replace Abstract: The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growin...
The paper introduces Relative Patch Response Learning (PRL) to detect AI‑generated images by comparing patch responses across two mixed views of aligned real‑generated image pairs. PRL uses a relative response objective, a reference coherence objective, and an area ranking objective to address contextual shifts caused by self‑attention, providing precise supervision for patch‑level detection. Experiments show PRL outperforms prior methods by 4.3% and 5.9% in average balanced accuracy on standard and in‑the‑wild benchmarks.
The paper introduces a multi‑view, confusion‑guided ensemble framework for synthetic image attribution, combining FFT‑ConvNeXt, DINOv2, CLIP, and Xception to capture frequency, semantic, and forensic cues. Extensive data augmentation simulates realistic post‑processing, while a binary expert classifier and class‑adaptive confidence calibration address ambiguities between similar diffusion models. The approach achieved 99.53% on the public leaderboard and 99.20% on the private leaderboard for the ICANN 2026 DLMMDD Workshop challenge.
arXiv:2510. 05740v2 Announce Type: replace-cross Abstract: The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images.
arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.
arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.
arXiv:2608. 12876v1 Announce Type: cross Abstract: Detecting AI-generated images is only half the task: a deployed detector must also justify its verdict, yet existing detectors inherit three failure modes from their training data: real and fake images collected from different sources invite provenance shortcuts, supervised explanation corpora teach templated rationales, and a static forgery corpus leaves the decision boundary standing still while generators keep moving.
The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images.
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.
arXiv:2609.07670v1 Announce Type: cross Abstract: The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, dee...
arXiv:2511. 04260v3 Announce Type: replace-cross Abstract: The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems.
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.