Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors

• URL: http://arxiv.org/abs/2111.07468v1
• Date: Sun, 14 Nov 2021 22:50:39 GMT
• Title: Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors
• Authors: Yuhang Lu, Evgeniy Upenik, Touradj Ebrahimi
• Abstract summary: A large number of deepfake detectors have been proposed recently in order to identify such content. Deepfakes are increasingly popular in both good faith applications such as in entertainment and maliciously intended manipulations such as in image and video forgery. This paper proposes a more rigorous and systematic framework to assess the performance of deepfake detectors in more realistic situations.
• Score: 11.881119750753648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfakes are becoming increasingly popular in both good faith applications such as in entertainment and maliciously intended manipulations such as in image and video forgery. Primarily motivated by the latter, a large number of deepfake detectors have been proposed recently in order to identify such content. While the performance of such detectors still need further improvements, they are often assessed in simple if not trivial scenarios. In particular, the impact of benign processing operations such as transcoding, denoising, resizing and enhancement are not sufficiently studied. This paper proposes a more rigorous and systematic framework to assess the performance of deepfake detectors in more realistic situations. It quantitatively measures how and to which extent each benign processing approach impacts a state-of-the-art deepfake detection method. By illustrating it in a popular deepfake detector, our benchmark proposes a framework to assess robustness of detectors and provides valuable insights to design more efficient deepfake detectors.

Related papers

• Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
  Speech deepfakes pose a significant threat to personal security and content authenticity. We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
  arXiv  Detail & Related papers  (2024-09-24T13:24:03Z)
• Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection [57.646582245834324]
  We propose a simple yet effective deepfake detector called LSDA. It is based on a idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary. We show that our proposed method is surprisingly effective and transcends state-of-the-art detectors across several widely used benchmarks.
  arXiv  Detail & Related papers  (2023-11-19T09:41:10Z)
• Facial Forgery-based Deepfake Detection using Fine-Grained Features [7.378937711027777]
  Facial forgery by deepfakes has caused major security risks and raised severe societal concerns. We formulate deepfake detection as a fine-grained classification problem and propose a new fine-grained solution to it. Our method is based on learning subtle and generalizable features by effectively suppressing background noise and learning discriminative features at various scales for deepfake detection.
  arXiv  Detail & Related papers  (2023-10-10T21:30:05Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection [96.539862328788]
  Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. We propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF) Experiment results on several public face forgery datasets demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T08:58:49Z)
• MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection [81.59191603867586]
  Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery. The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations. We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
  arXiv  Detail & Related papers  (2023-07-06T02:32:08Z)
• Assessment Framework for Deepfake Detection in Real-world Situations [13.334500258498798]
  Deep learning-based deepfake detection methods have exhibited remarkable performance. The impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. A more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings.
  arXiv  Detail & Related papers  (2023-04-12T19:09:22Z)
• Impact of Video Processing Operations in Deepfake Detection [13.334500258498798]
  Digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. Deep learning-based deepfake detection methods have been developed and have shown impressive results. The performance of these detectors is often evaluated using benchmarks that hardly reflect real-world situations.
  arXiv  Detail & Related papers  (2023-03-30T09:24:17Z)
• A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions [13.334500258498798]
  Deep convolutional neural networks have achieved exceptional results on multiple detection and recognition tasks. The impact of conventional distortions and processing operations found in imaging such as compression, noise, and enhancement are not sufficiently studied. This paper proposes a more effective data augmentation scheme based on real-world image degradation process.
  arXiv  Detail & Related papers  (2022-03-22T15:16:54Z)
• A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection [11.287342793740876]
  This paper proposes a rigorous framework to assess performance of learning-based detectors in more realistic situations. Inspired by the assessment results, a data augmentation strategy based on natural image degradation process is designed.
  arXiv  Detail & Related papers  (2022-03-22T15:03:56Z)
• Representative Forgery Mining for Fake Face Detection [52.896286647898386]
  We propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery.
  arXiv  Detail & Related papers  (2021-04-14T03:24:19Z)

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.