DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

• URL: http://arxiv.org/abs/2307.01426v2
• Date: Sat, 28 Oct 2023 10:05:52 GMT
• Title: DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
• Authors: Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, Baoyuan Wu
• Abstract summary: A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
• Score: 55.70982767084996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench.

Related papers

• DF40: Toward Next-Generation Deepfake Detection [62.073997142001424]
  existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset and testing them on other prevalent deepfake datasets. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? We construct a highly diverse deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques.
  arXiv  Detail & Related papers  (2024-06-19T12:35:02Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
  PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
  arXiv  Detail & Related papers  (2024-03-27T14:34:29Z)
• 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)
• Locate and Verify: A Two-Stream Network for Improved Deepfake Detection [33.50963446256726]
  Current deepfake detection methods are typically inadequate in generalizability. We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts evidence. We also propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations.
  arXiv  Detail & Related papers  (2023-09-20T08:25:19Z)
• Self-Supervised Graph Transformer for Deepfake Detection [1.8133635752982105]
  Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset. Deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. This study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability.
  arXiv  Detail & Related papers  (2023-07-27T17:22:41Z)
• A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials [97.69553832500547]
  This paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. We exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, to the continual deepfake detection problem.
  arXiv  Detail & Related papers  (2022-05-11T13:07:19Z)
• Multi-attentional Deepfake Detection [79.80308897734491]
  Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. We propose a new multi-attentional deepfake detection network. Specifically, it consists of three key components: 1) multiple spatial attention heads to make the network attend to different local parts; 2) textural feature enhancement block to zoom in the subtle artifacts in shallow features; 3) aggregate the low-level textural feature and high-level semantic features guided by the attention maps.
  arXiv  Detail & Related papers  (2021-03-03T13:56:14Z)

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.