The paper presents lightweight architectures for detecting GAN-generated synthetic faces, comparing a compact Swin Transformer, pre‑trained Swin‑Tiny and Swin‑Small models, and a hybrid EfficientNet‑B0 + Swin Transformer. Using the 140K Real and Fake Faces dataset, the hybrid model achieved 99% accuracy and 99.44% recall on 5,000 test images, outperforming both pure Swin variants and a CNN‑only baseline. The study demonstrates that combining hierarchical CNN features with shifted‑window self‑attention yields an efficient, computationally lightweight detection method.
By Sejuti Basu, Ashima Sood, Vijay Kumar, Sahil Sharma
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:2502. 19716v3 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source content.
By Qijie Xu, Can Wang, Jiawei Chen, Siwei Lyu, Defang Chen
arXiv:2511. 04260v3 Announce Type: replace-cross Abstract: The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems.
By Claudio Giusti, Luca Guarnera, Sebastiano Battiato
arXiv:2603.14005v2 Announce Type: replace
Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using...
By Ming-Hui Liu, Harry Cheng, Xin Luo, Xin-Shun Xu, Mohan S. Kankanhalli
The paper introduces MoE-JEPA, a dual‑stream deepfake detection model that combines a V‑JEPA backbone with a Residual Mixture‑of‑Experts mechanism and a noise stream branch. It further incorporates a Gated Attention Multiple Instance Learning module to refine spatial semantic understanding. On the SID‑Set benchmark, MoE‑JEPA achieves a new state‑of‑the‑art accuracy of 95.54%, outperforming much larger models.
By Simone Teglia, Irene Amerini
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: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: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.
By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan
arXiv:2411. 19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content.
By Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existing methods have grown as elaborate as the genera...
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...
By Xuechao Zou, Yi Zhou, Kai Li, Shun Zhang, Yuhui Chen, Congyan Lang, Junliang Xing