arXiv:2608. 03557v1 Announce Type: cross Abstract: Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models.
By Malena Loza, Felipe Grijalva, Eva Milara, Luis Bote-Curiel, Francisco J. Lara-Abelenda, David Chushig-Muzo
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
arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.
By Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian
arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.
By Felix Krumbiegel, Juniper Tyree, Michael Boy, Petri Clusius, Andreas Rupp
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:2609.01027v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need ima...
By Ruslan Rozumnyi, Mat\v{e}j Such\'anek, Tom\'a\v{s} Voj\'i\v{r}, Kl\'ara Janou\v{s}kov\'a, Ji\v{r}\'i Matas