The paper introduces FADNet, a face authenticity detector that treats forgery detection as a one‑class classification problem, training only on authentic faces. It uses Evidential Deep Learning to measure uncertainty and a pseudo‑forgery generator to sharpen decision boundaries. Experiments on DF40 and ASFD show FADNet surpasses state‑of‑the‑art methods with 96.63% accuracy and 98.83% precision.
By Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu, Zhenxing Qian, Xinpeng Zhang, Zaiwang Gu
The paper presents an explainable deep‑learning workflow for detecting deepfake images, comparing a compact convolutional model trained from scratch with a fine‑tuned ImageNet‑pretrained MobileNetV2 model. Using a balanced dataset of 140,000 real and fake faces, the pretrained model achieved 95.90% accuracy, outperforming the from‑scratch model’s 84.15% accuracy. Gradient‑weighted class activation mapping (Grad‑CAM) was employed to highlight image regions influencing predictions, and a Flask prototype was built for side‑by‑side inspection.
By Saad Korchi, Abderrahim Waga
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
By Omid Ahmadieh, Nima Karimian
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:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
By Xuechao Zou, Shun Zhang, Kai Li, Yi Zhou, Xinyu Sun, Yuhui Chen, Zhe Wu, Congyan Lang, Junliang Xing
The paper introduces DDL, a large-scale dataset for deepfake detection and localization, comprising over 1.4 million forged samples generated by 80 distinct deepfake methods. It features comprehensive coverage of generation architectures, varied manipulation modes, diverse scenarios and modalities, and provides fine-grained spatial masks and temporal segments for each forgery. These enhancements aim to offer a more challenging benchmark and support the development of next-generation deepfake detection, localization, and interpretability methods.
By Changtao Miao, Yi Zhang, Weize Gao, Zhiya Tan, Weiwei Feng, Man Luo, Jianshu Li, Ajian Liu, Yunfeng Diao, Qi Chu, Tao Gong, Zhe Li, Weibin Yao, Joey Tianyi Zhou
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:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.
By Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed, Harrison J Rosenberg, Ramya Korlakai Vinayak, Kassem Fawaz
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
By Omid Ahmadieh, Nima Karimian
FORGE is a forensic deepfake analysis system that provides region‑grounded natural language explanations for image manipulations. It addresses the inductive bias mismatch of multimodal large language models by adding a Vision‑Only Model trained on dense patch prediction, allowing the language model to interleave tokens with preserved spatial correspondence. Across face‑manipulated and fully synthetic content, FORGE delivers fine‑grained attribute queries and outperforms in‑domain baselines, with region‑specific evaluation and human studies confirming explanation faithfulness.
By Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury
The paper introduces IMFD, an end‑to‑end multi‑face forgery detector that uses instruction‑based Large Vision‑Language Models (LVLMs). IMFD jointly localizes faces and predicts forgery labels in a single stage, explicitly incorporating predicted face bounding boxes into the textual instruction to improve grounding and detection. Experiments on converted multi‑face forgery datasets show that IMFD outperforms several state‑of‑the‑art methods.
By Dasom Choi, Sangjun Moon, Hyeongchan Im, Jaeeon Park, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura
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.
By Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida, Sebastiano Battiato