arXiv:2606. 03348v1 Announce Type: cross Abstract: Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility.
arXiv:2607. 22745v1 Announce Type: cross Abstract: Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation.
With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prior work has explored vision-language model (VLM)-based synthetic image detection, these evaluations typically consider images in isolation.
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...
arXiv:2606. 25375v2 Announce Type: replace-cross Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud.
ASAP is an interactive visualization system that helps users identify and analyze deceptive patterns in AI‑generated images. It uses a CLIP‑adapted image encoder to produce interpretable representations and generates masks that highlight influential pixel regions, enabling influence measurement of key deceptive features. The system integrates these techniques into a dashboard for quantifying authenticity‑indicative patterns across collections of authentic and AI‑generated images, supporting comparative analysis of different generative models such as GANs and diffusion models, and its effectiveness is demonstrated through a user study and benchmark applications.
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.
The paper investigates how different multimodal design choices affect the performance of misinformation detection systems. Using over 3,375 experiments across three benchmark datasets and various pre‑trained vision and language models, the authors systematically compare design options and conduct robustness analyses. The study offers practical guidance on which choices improve detection, when they may fail silently, and which pipeline components most influence model behavior, addressing four key research questions.
arXiv:2608. 16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable.
arXiv:2609.39662v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity j...
arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains...
arXiv:2603. 28583v2 Announce Type: replace-cross Abstract: Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations.
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.