arXiv Computer Vision
DeepfakeGenome: Toward Next-Generation Deepfake Attribution
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 21
Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
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.
arXiv Computer Vision
Oct 19
Deep Learning for Deepfake Image Detection: Comparative Model Evaluation and Gradient-Based Visual Attribution
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.
arXiv AI
Aug 3
DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
arXiv AI
Sep 3
Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics
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.
arXiv AI
Aug 10
Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection
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.
arXiv Computer Vision
Oct 9
DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios
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.