Abstract
Media synthesis and manipulation has reached unprecedented levels of realism owing to the proliferation of deep learning. Deepfake has been the de-facto tool for media manipulation. Although this technology has potential in the entertainment industry, its threats include political manipulation and bypassing biometric security systems. As a result, deepfake detection has garnered widespread attention among research communities. The intuition is to use deep learning to fix the problems created by deep learning. Although convolutional neural networks have shown their dominance in the filed of pattern recognition, the receptive field-model size dilemma still persists along with the lack of interpretation for such models. While the traditional Gabor function was proposed to fix these problems, it can only generate limited linear Gabor filters which makes it optimal for limited data and applications. The contribution of this paper is quadruple: (i) proposing a unified Gabor function capable of generating linear, elliptical, and circular Gabor filters. (ii) leveraging the back-propagation learning framework to incorporate the proposed function in convolutional neural networks and generate adaptive Gabor filters. (iii) presenting a dual scale large receptive field network for deepfake image recognition. (iv) demonstrating where the proposed model stands in terms of performance and architecture size compared to state-of-the-art models. The proposed model is evaluated on four benchmark datasets: Celeb-DF (v2), DeepFake Detection Challenge Preview, FaceForensics++ and Wilddeepfake. Experimental results show that the proposed adaptive Gabor filters reduce the model size by 64.9% compared to adaptive weighted filters without performance reduction.
Highlights
The recent developments in deep generative models (DGMs), variational autoencoders [1] and Generative Adversarial Networks [2], has enabled media synthesis and manipulation to reach unprecedented levels of realism
While adaptive Gabor filters (AGFs) produced from the proposed function could be applied in a variety of vision-related applications, we propose a compact architecture based on dual scale large receptive fields and self-attention for deepfake image recognition to demonstrate the effectiveness of AGFs
While the proposed function can be used to construct deep architectures that solely consist of convolutional layers based on AGFS, it can be simultaneously used with other convolutional layers that are based on Adaptive Weighted Filters (AWFs) within a single architecture as in the proposed dual scale large receptive field network (DSLRFN) for deepfake image recognition
Summary
The recent developments in deep generative models (DGMs), variational autoencoders [1] and Generative Adversarial Networks [2], has enabled media synthesis and manipulation to reach unprecedented levels of realism. In order to extract distinctive feature at different scales and orientations from limited training data, a combination of both fixed Gabor ensemble filter and AWFs were proposed for hyperspectral image classification [21]. In [30], Feng et al incorporated triplet loss function in the feature extraction stage of the deep learning model followed by a linear classification network to discriminate the learned contrastive features between real and fake face images. In addition to the multi-scale transformer that detects local inconsistency at different spatial levels, frequency information is leveraged to enhance the robustness of the model to image compression. In order to fully utilize all these shape for effective feature extraction, we develop a unified phase-induced diverse Gabor function GΨ(x, y) by introducing the novel parameter α, as follows: GΨ(x, y) = K exp(jP ). 2 illustrates special cases of the diverse Gabor function by varying the values of α and γ
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