WildFakeToolBench is a benchmark suite comprising 156 executable image‑processing tools sourced from 220 public repositories across 142 domains. The suite evaluates six state‑of‑the‑art AI‑generated image detectors on 105 images processed by these tools, revealing that some tools can evade detectors with success rates up to 100%. The study demonstrates that web‑sourced tools pose significant in‑the‑wild risks and underscores the need for robust detector evaluation frameworks.
By Mingjie Li, Yukun Jiang, Yicong Tan, Yang Zhang
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. 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.
By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan
arXiv:2609.10002v1 Announce Type: new
Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and t...
By Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
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:2512.17730v2 Announce Type: replace
Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
By Yichen Jiang, Mohammed Talha Alam, Sohail Ahmed Khan, Duc-Tien Dang-Nguyen, Fakhri Karray