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: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.
arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.
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:2608. 03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped.
arXiv:2608. 04720v4 Announce Type: replace Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas.
arXiv:2609.14316v1 Announce Type: new Abstract: Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustwo...
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed.
DailyBench is a unified benchmark designed to evaluate AI-generated image detectors on both modern full-image synthesis and object-level manipulation. It comprises two subsets: FakeBench, featuring high‑quality images from recent open‑source and commercial generative models, and ManipulationBench, containing subtle local edits applied to real images using advanced image‑conditional models. Experiments show that detectors with high accuracy on older datasets perform poorly on DailyBench, revealing significant robustness gaps.
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.
arXiv:2609.38010v1 Announce Type: cross Abstract: Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety m...
The paper investigates patch‑wise supervision for detecting AI‑generated images, proposing a shared backbone that classifies explicit crops with individual losses and averages patch probabilities only during inference. This approach eliminates the need for handcrafted residual filtering or learned image‑level fusion modules. Experiments across single‑patch selection, multiple generator collections, and four CNN and Transformer backbones show that patch‑wise variants outperform whole‑image counterparts on the GenImage dataset, while also exploring factors such as supervision granularity, source resolution, crop size, and inference coverage.
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.