Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection

TL;DR

This paper introduces an enhanced Xception model with dual attention and feature fusion mechanisms to improve face forgery detection, especially on low-quality and diverse images, demonstrating superior performance over existing methods.

Contribution

The paper proposes a novel face forgery detection model that integrates dual attention and feature fusion into Xception, enhancing feature extraction and classification capabilities.

Findings

Outperforms baseline Xception and related methods

Effective on multiple Deepfake datasets

Shows improved generalization and robustness

Abstract

With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of the face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification the ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability.