Block shuffling learning for Deepfake Detection

• URL: http://arxiv.org/abs/2202.02819v2
• Date: Thu, 13 Jul 2023 09:13:40 GMT
• Title: Block shuffling learning for Deepfake Detection
• Authors: Sitong Liu, Zhichao Lian, Siqi Gu, Liang Xiao
• Abstract summary: Deepfake detection methods based on convolutional neural networks (CNN) have demonstrated high accuracy. These methods often suffer from decreased performance when faced with unknown forgery methods and common transformations. We propose a novel block shuffling regularization method to address this issue.
• Score: 9.180904212520355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deepfake detection methods based on convolutional neural networks (CNN) have demonstrated high accuracy. \textcolor{black}{However, these methods often suffer from decreased performance when faced with unknown forgery methods and common transformations such as resizing and blurring, resulting in deviations between training and testing domains.} This phenomenon, known as overfitting, poses a significant challenge. To address this issue, we propose a novel block shuffling regularization method. Firstly, our approach involves dividing the images into blocks and applying both intra-block and inter-block shuffling techniques. This process indirectly achieves weight-sharing across different dimensions. Secondly, we introduce an adversarial loss algorithm to mitigate the overfitting problem induced by the shuffling noise. Finally, we restore the spatial layout of the blocks to capture the semantic associations among them. Extensive experiments validate the effectiveness of our proposed method, which surpasses existing approaches in forgery face detection. Notably, our method exhibits excellent generalization capabilities, demonstrating robustness against cross-dataset evaluations and common image transformations. Especially our method can be easily integrated with various CNN models. Source code is available at \href{https://github.com/NoWindButRain/BlockShuffleLearning}{Github}.

Related papers

• Robust Network Learning via Inverse Scale Variational Sparsification [55.64935887249435]
  We introduce an inverse scale variational sparsification framework within a time-continuous inverse scale space formulation. Unlike frequency-based methods, our approach not only removes noise by smoothing small-scale features. We show the efficacy of our approach through enhanced robustness against various noise types.
  arXiv  Detail & Related papers  (2024-09-27T03:17:35Z)
• Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection [41.35861722481721]
  Deepfake threats to society and cybersecurity have provoked significant public apprehension. This paper introduces an elegantly simple yet effective strategy named Thumbnail Layout (TALL) TALL transforms a video clip into a pre-defined layout to realize the preservation of spatial and temporal dependencies.
  arXiv  Detail & Related papers  (2024-03-15T12:48:44Z)
• Self-supervised Transformer for Deepfake Detection [112.81127845409002]
  Deepfake techniques in real-world scenarios require stronger generalization abilities of face forgery detectors. Inspired by transfer learning, neural networks pre-trained on other large-scale face-related tasks may provide useful features for deepfake detection. In this paper, we propose a self-supervised transformer based audio-visual contrastive learning method.
  arXiv  Detail & Related papers  (2022-03-02T17:44:40Z)
• Saliency Constrained Arbitrary Image Style Transfer using SIFT and DCNN [22.57205921266602]
  When common neural style transfer methods are used, the textures and colors in the style image are usually transferred imperfectly to the content image. This paper proposes a novel saliency constrained method to reduce or avoid such effects. The experiments show that the saliency maps of source images can help find the correct matching and avoid artifacts.
  arXiv  Detail & Related papers  (2022-01-14T09:00:55Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Learning Discriminative Shrinkage Deep Networks for Image Deconvolution [122.79108159874426]
  We propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy.
  arXiv  Detail & Related papers  (2021-11-27T12:12:57Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Improving DeepFake Detection Using Dynamic Face Augmentation [0.8793721044482612]
  Most publicly available DeepFake detection datasets have limited variations. Deep neural networks tend to overfit to the facial features instead of learning to detect manipulation features of DeepFake content. We introduce Face-Cutout, a data augmentation method for training Convolutional Neural Networks (CNN) to improve DeepFake detection.
  arXiv  Detail & Related papers  (2021-02-18T20:25:45Z)
• Image Restoration by Deep Projected GSURE [115.57142046076164]
  Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
  arXiv  Detail & Related papers  (2021-02-04T08:52:46Z)
• A Compact Deep Learning Model for Face Spoofing Detection [4.250231861415827]
  presentation attack detection (PAD) has received significant attention from research communities. We address the problem via fusing both wide and deep features in a unified neural architecture. The procedure is done on different spoofing datasets such as ROSE-Youtu, SiW and NUAA Imposter.
  arXiv  Detail & Related papers  (2021-01-12T21:20:09Z)
• Solving Inverse Problems With Deep Neural Networks -- Robustness Included? [3.867363075280544]
  Recent works have pointed out instabilities of deep neural networks for several image reconstruction tasks. In analogy to adversarial attacks in classification, it was shown that slight distortions in the input domain may cause severe artifacts. This article sheds new light on this concern, by conducting an extensive study of the robustness of deep-learning-based algorithms for solving underdetermined inverse problems.
  arXiv  Detail & Related papers  (2020-11-09T09:33:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.