Multiple contexts and frequencies aggregation network for deepfake detection
Zifeng Li, Wenzhong Tang, Shijun Gao, Yanyang Wang, Shuai Wang, Bushra Zafar, Bushra Zafar, Daniel Parkes, Feng Ding, Feng Ding
TL;DR
This paper introduces MkfaNet, a new network for detecting deepfakes by combining spatial and frequency features, achieving strong performance across multiple benchmarks.
Contribution
The novel contribution is MkfaNet, which integrates multi-kernel and multi-frequency aggregation modules for improved deepfake detection.
Findings
MkfaNet achieves an AUC of 0.9591 in within-domain deepfake detection.
The model outperforms state-of-the-art methods in cross-domain evaluations with an AUC of 0.7963.
The proposed network maintains high computational efficiency while offering robustness against diverse deepfake manipulations.
Abstract
Deepfake detection faces increasing challenges since the fast growth of generative models in developing massive and diverse Deepfake technologies. Recent advances rely on introducing heuristic features from spatial or frequency domains rather than modeling general forgery features within backbones. To address this issue, we turn to the backbone design with two intuitive priors from spatial and frequency detectors, i.e., learning robust spatial attributes and frequency distributions that are discriminative for real and fake samples. To this end, we propose an efficient network for face forgery detection named MkfaNet, which consists of two core modules. For spatial contexts, we design a Multi-Kernel Aggregator that adaptively selects organ features extracted by multiple convolutions for modeling subtle facial differences between real and fake faces. For the frequency components, we…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection · Face recognition and analysis