A Timely Survey on Vision Transformer for Deepfake Detection
- URL: http://arxiv.org/abs/2405.08463v1
- Date: Tue, 14 May 2024 09:33:04 GMT
- Title: A Timely Survey on Vision Transformer for Deepfake Detection
- Authors: Zhikan Wang, Zhongyao Cheng, Jiajie Xiong, Xun Xu, Tianrui Li, Bharadwaj Veeravalli, Xulei Yang,
- Abstract summary: Vision Transformer (ViT)-based approaches showcase superior performance in generality and efficiency.
This survey aims to equip researchers with a nuanced understanding of ViT's pivotal role in deepfake detection.
- Score: 11.410817278428533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, the rapid advancement of deepfake technology has revolutionized content creation, lowering forgery costs while elevating quality. However, this progress brings forth pressing concerns such as infringements on individual rights, national security threats, and risks to public safety. To counter these challenges, various detection methodologies have emerged, with Vision Transformer (ViT)-based approaches showcasing superior performance in generality and efficiency. This survey presents a timely overview of ViT-based deepfake detection models, categorized into standalone, sequential, and parallel architectures. Furthermore, it succinctly delineates the structure and characteristics of each model. By analyzing existing research and addressing future directions, this survey aims to equip researchers with a nuanced understanding of ViT's pivotal role in deepfake detection, serving as a valuable reference for both academic and practical pursuits in this domain.
Related papers
- A Survey of Defenses against AI-generated Visual Media: Detection, Disruption, and Authentication [15.879482578829489]
Deep generative models have demonstrated impressive performance in various computer vision applications.
These models may be used for malicious purposes, such as misinformation, deception, and copyright violation.
This paper provides a systematic and timely review of research efforts on defenses against AI-generated visual media.
arXiv Detail & Related papers (2024-07-15T09:46:02Z) - Video Anomaly Detection in 10 Years: A Survey and Outlook [10.143205531474907]
Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring.
This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches.
arXiv Detail & Related papers (2024-05-29T17:56:31Z) - Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
We discuss the recent advances in deep learning-based object pose estimation.
Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
arXiv Detail & Related papers (2024-05-13T14:44:22Z) - Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions.
This survey comprehensively reviews the latest developments in deepfake generation and detection.
We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
arXiv Detail & Related papers (2024-03-26T17:12:34Z) - GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency
Learning [63.547321642941974]
We introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model.
Experiment results reveal that our proposed GazeForensics outperforms the current state-of-the-art methods.
arXiv Detail & Related papers (2023-11-13T04:48:33Z) - Improving Cross-dataset Deepfake Detection with Deep Information
Decomposition [57.284370468207214]
Deepfake technology poses a significant threat to security and social trust.
Existing detection methods suffer from sharp performance degradation when faced with cross-dataset scenarios.
We propose a deep information decomposition (DID) framework in this paper.
arXiv Detail & Related papers (2023-09-30T12:30:25Z) - Contrastive Pseudo Learning for Open-World DeepFake Attribution [67.58954345538547]
We introduce a new benchmark called Open-World DeepFake (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios.
We propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set.
arXiv Detail & Related papers (2023-09-20T08:29:22Z) - Forgery-aware Adaptive Vision Transformer for Face Forgery Detection [57.56537940216884]
We propose a Forgery-aware Adaptive Vision Transformer (FA-ViT)
In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge.
Two specially designed components, the Local-aware Forgery (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge.
arXiv Detail & Related papers (2023-09-20T06:51:11Z) - Combating Advanced Persistent Threats: Challenges and Solutions [20.81151411772311]
The rise of advanced persistent threats (APTs) has marked a significant cybersecurity challenge.
Provenance graph-based kernel-level auditing has emerged as a promising approach to enhance visibility and traceability.
This paper proposes an efficient and robust APT defense scheme leveraging provenance graphs, including a network-level distributed audit model for cost-effective lateral attack reconstruction.
arXiv Detail & Related papers (2023-09-18T05:46:11Z) - Poisoning Attacks and Defenses on Artificial Intelligence: A Survey [3.706481388415728]
Data poisoning attacks represent a type of attack that consists of tampering the data samples fed to the model during the training phase, leading to a degradation in the models accuracy during the inference phase.
This work compiles the most relevant insights and findings found in the latest existing literatures addressing this type of attacks.
A thorough assessment is performed on the reviewed works, comparing the effects of data poisoning on a wide range of ML models in real-world conditions.
arXiv Detail & Related papers (2022-02-21T14:43:38Z) - Deep Learning meets Liveness Detection: Recent Advancements and
Challenges [3.2011056280404637]
We present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017.
We cover predominant public datasets for FAS in chronological order, their evolutional progress, and the evaluation criteria.
arXiv Detail & Related papers (2021-12-29T19:24:58Z)
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.