Spectra is a deepfake detection framework that uses implicit neural representations (INRs) to analyze how an image is reconstructed, producing multi-level reconstruction heatmaps. These heatmaps are fused with semantic visual features via a bidirectional mechanism, enabling the model to detect forgery cues that are less tied to specific generation pipelines. Experiments on several deepfake benchmarks show that Spectra outperforms detectors relying only on RGB appearance or semantic representations, and it also extends to face presentation attack detection.
By Dhananjaya Jayasundara, Kartik Narayan, Vishal M. Patel
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
The paper introduces the Deepfake Face Artifact Taxonomy (DFAT) and uses it to create both general-purpose and model-specific training materials for detecting deepfake face images. An online experiment with 524 participants tested detection accuracy across five generative models—three GANs, one diffusion model, and one multimodal large language model (GPT‑5)—showing that detection performance varies strongly by model and that model‑specific training yields greater gains than general training. A five‑month follow‑up with 342 participants revealed stable detection accuracy and no increase in perceived stress, suggesting the training’s durability.
By Zhiqing Guo, Lixin Jia, Luning Sun, Xiaowei Chen, Guofeng Yu, Liejun Wang, Gaobo Yang, David Stillwell, Thorsten Holz, Shujun Li
arXiv:2609.14437v1 Announce Type: cross
Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
By Arya Pulkit, Aditya Ruhela, Akarshan Kapoor, Arnav Bhavsar
arXiv:2510.16320v2 Announce Type: replace
Abstract: This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the...
By Wenhao Wang, Jusheng Zhang, Longqi Cai, Taihong Xiao, Yuxiao Wang, Ming-Hsuan Yang
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
arXiv:2608.23363v1 Announce Type: cross
Abstract: Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deep...
By Vlad Hondru, Florinel Alin Croitoru, Iuliana Georgescu, A. Sophia Koepke, Radu Tudor Ionescu
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 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
The paper introduces AdaGate-DF, an adaptive gated deepfake detection framework designed for low-resolution and resource-constrained environments. By leveraging image-quality cues, the system routes samples through a dual multi-exit architecture, allowing high-quality images to exit earlier and reduce computational load. Evaluations on Celeb-DF and FaceForensics++ show that AdaGate-DF outperforms existing models such as MaD-CoRN and DefakeHop++ while maintaining low inference latency and robust performance across varying resolutions and class imbalance scenarios.
By Vaishnavi Sen, Cody Laurie, Rashida Hasan
The paper introduces Motion‑Aware Deepfake (MAD) videos, a new class of deepfakes generated by pose‑guided diffusion models that can animate entire human figures. To study this threat, the authors build the first MAD‑specific benchmark with 1.5 million frames from 1,363 real and 30,122 synthetic videos, and analyze the subtle high‑frequency artifacts and spectral fingerprints that arise from limited input frames. Leveraging these insights, they propose MoDA, a defense framework that combines spatial semantics with steganalysis‑rich frequency features, achieving high detection and attribution accuracy across in‑distribution, cross‑dataset, and zero‑shot scenarios, and demonstrating robustness against adaptive attacks.
By Fazhong Liu, Yan Meng, Tian Dong, Guoxing Chen, Haojin Zhu
RLDF (Region-Level Deepfake) is the first dataset designed to evaluate deepfake detection on partial facial manipulations. It contains over 2 million partial face‑swap images generated with eight face‑swapping methods across seven facial regions, including both discrete (eye, nose, mouth, eyebrow) and composite regions. The authors benchmark 12 state‑of‑the‑art detectors on five cross‑dataset protocols, revealing a 10.5 % drop in AUC and 10 % drop in accuracy for region‑level detection, and propose a mask‑guided cross‑attention model as a baseline.
By Shukesh Reddy, Abhijit Das