The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
By Simiao Ren (Neo), Yuchen Zhou (Neo), Xingyu Shen (Neo), Kidus Zewde (Neo), Tommy Duong (Neo), George Huang (Neo), Hatsanai (Neo), Tiangratanakul (Dennis), Tsang (Dennis), Ng (Dennis), En Wei, Jiayu Xue
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
arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.
By Ken Jon Miyachi, Dylan Uys
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.
By Yi-Zhi Wang, Yichen Xiao, Linan Yue, Weibo Gao, Yichao Du, Pengfei Fang, Shimin Di, Min-Ling Zhang
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.
By Pei Li, Sihan Chen, Delong Ran, Tianshuo Cong
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.
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.
By Amirtaha Amanzadi, Zahra Dehghanian, Hamid Beigy, Hamid R. Rabiee
arXiv:2410. 01574v4 Announce Type: replace-cross Abstract: The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse.
By Sina Mavali, Jonas Ricker, David Pape, Asja Fischer, Lea Sch\"onherr
The paper introduces LAION-Mobile, a 1‑million‑image dataset of modern smartphone photos with EXIF metadata, to test deepfake detectors in a realistic computational‑photography setting. Twelve state‑of‑the‑art detectors were evaluated on a 9,115‑image subset, revealing that none achieved an AUC above 0.624 on AI‑generated content and that many detectors’ false‑alarm rates spike when thresholds are recalibrated for modern smartphone imagery. The study shows that current detectors fail to maintain both high detection performance and low false‑alarm rates on contemporary smartphone photos, highlighting a critical gap in existing evaluation benchmarks.
The paper introduces LAION-Mobile, a new dataset of roughly one million smartphone images with EXIF metadata, designed to test deepfake detectors on modern computational photography content. Twelve state‑of‑the‑art detectors were evaluated on a 9,115‑image subset, revealing that none achieved an AUC above 0.624 on AI‑generated images and five performed worse than chance. The study also shows that threshold calibration on legacy data can hide high false‑alarm rates, with some detectors flagging 17–91 % of real photos when recalibrated for modern content.
By Achim von Stryk, Janis Keuper
arXiv:2610.28087v1 Announce Type: new
Abstract: AI-generated images linked to news events and online claims can mislead audiences, enable fraud, or cause public controversy. We introduce WildAIGC-Ben...
By Zijian Yu, Hangwei Zhang, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang
DecisionRobust is a new benchmark that evaluates the robustness of JEV-style decision models to adversarial attacks across both text and vision inputs. It includes a variety of attack types—semantic text perturbations, image perturbations, and prompt injections—and uses external validity checks and cross-model candidate selection to curate evaluation sets. Experiments on 30 text and 17 vision model configurations reveal that vulnerabilities can transfer across models, robustness varies by task and attack, and label-consistency checks significantly reduce apparent attack success.
By Yifan Lan, Zhongye Liu, Yurui Chang, Hanyu Wang, Yongkang Du, Lu Lin, Jinghui Chen