ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis (2103.05630v2)

Abstract: The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis have become a pressing issue. However, existing face forgery datasets either have limited diversity or only support coarse-grained analysis. To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 videos), manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations) and annotations (6.3 million classification labels, 2.9 million manipulated area annotations and 221,247 temporal forgery segment labels). We perform extensive benchmarking and studies of existing face forensics methods and obtain several valuable observations.

ForgeryNet: A Benchmark for Comprehensive Forgery Analysis

The paper "ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis" introduces a substantial face forgery dataset aimed at advancing research in digital forgery detection and analysis. ForgeryNet is distinguished by its scale and diversity, offering a significant contribution to the field of face forgery research. As photorealistic image and video synthesis techniques continue to evolve, the demand for robust forgery analysis tools increases. The ForgeryNet dataset represents a strategic resource designed to fulfill this need by facilitating various forgery analysis tasks.

Dataset Composition and Features

ForgeryNet is remarkable for its sheer scale, consisting of 2.9 million images and 221,247 videos. The dataset is constructed with diverse manipulation methods covering 15 image-level and 8 video-level approaches. These methods are further classified into identity-remained and identity-replaced categories. The dataset includes perturbations from compression, transmission, and other processes that challenge traditional detection algorithms. The annotations provided cover 6.3 million classification labels, 2.9 million spatial forgery localization annotations, and 221,247 temporal forgery segment labels, enabling multi-dimensional forgery analysis tasks.

Task Design

ForgeryNet supports four primary forgery analysis tasks:

1. Image Forgery Classification: Enabling classifiers to distinguish between real and fake images using two-way, three-way, or $n$-way classification schemes.
2. Spatial Forgery Localization: Focused on identifying manipulated regions within images through pixel-by-pixel analysis.
3. Video Forgery Classification: Extending manipulation detection to video sequences, with random frame perturbations mimicking real-world attacks.
4. Temporal Forgery Localization: A novel task aimed at detecting manipulated segments within video streams, addressing real-world application scenarios where only portions of videos are manipulated.

Evaluation and Findings

Extensive benchmarking was conducted across various models, ranging from efficient architectures suitable for mobile applications to deep learning models capable of processing high-resolution inputs. The findings suggest that larger and more diverse datasets significantly boost forgery detection performance, evidenced by improved accuracy and Area Under Curve (AUC) metrics across several tested models. Additionally, results highlight better generalization capabilities when models trained on ForgeryNet are tested against other popular datasets.

Theoretical and Practical Implications

ForgeryNet's comprehensive design provides invaluable insights and a testing ground for developing next-generation algorithms that address the multifaceted challenges of forgery detection. Beyond its immediate utility for face forgery detection, the methods and findings derived from using ForgeryNet have implications for broader image authenticity analysis tasks. Theoretical implications rest on expanding understanding of convolutional and adversarial network capabilities in distinguishing real from manipulated media.

Future Work

The paper encourages future research to further exploit ForgeryNet for enhancing real-world forgery detection systems. It invites contributions in developing novel forgery approaches, which could be incorporated into the dataset to continuously evolve this benchmark. Additionally, ongoing analysis should aim at improving defense strategies against increasingly sophisticated forgery techniques, ensuring robust media authenticity verification.

In conclusion, ForgeryNet sets a new standard for datasets in the domain of digital forgery analysis by combining scale, diversity, and comprehensive annotations with a forward-thinking approach to both practical applications and theoretical exploration.