Recurrent Convolutional Strategies for Face Manipulation Detection in Videos (1905.00582v3)

Abstract: The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the best strategy for combining variations in these models along with domain specific face preprocessing techniques through extensive experimentation to obtain state-of-the-art performance on publicly available video-based facial manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. Evaluation is performed on the recently introduced FaceForensics++ dataset, improving the previous state-of-the-art by up to 4.55% in accuracy.

Recurrent Convolutional Strategies for Face Manipulation Detection in Videos: A Summary

This paper addresses the problem of detecting manipulated faces in video streams, an area of growing concern due to the proliferation of deepfake content and other synthetic media used to disseminate misinformation. While many methods have been developed for still images, this paper focuses on leveraging temporal information inherent in videos through the use of recurrent convolutional models, presenting a novel approach that improves upon previous state-of-the-art methods on publicly available benchmarks.

The authors propose a methodology that integrates recurrent convolutional neural networks (CNNs) with face preprocessing techniques to detect face manipulations such as Deepfake, Face2Face, and FaceSwap within video data. The research utilizes the FaceForensics++ dataset for evaluation, achieving up to a 4.55% improvement in accuracy compared to prior state-of-the-art methods.

Key Contributions and Methodology

The paper's main contribution is the introduction of a two-step processing pipeline:

1. Face Preprocessing: The first phase involves detecting, cropping, and aligning faces across a sequence of frames to mitigate issues related to the rigid motion of the face. The paper evaluates two alignment techniques: explicit alignment using facial landmarks and implicit alignment through a Spatial Transformer Network (STN).
2. Recurrent Convolutional Model: The aligned face crops are then inputted into a recurrent convolutional model that captures temporal inconsistencies across frames, leveraging the sequential nature of video data. This technique is designed to identify subtle artifacts that indicate manipulation, which would typically go undetected in individual frames.

The recurrent component utilizes Gated Recurrent Units (GRUs) to process features extracted from CNN architectures—specifically, ResNet and DenseNet. DenseNet, when used with landmark-based alignment and bidirectional recurrence, provided the best performance results, suggesting its suitability in exploiting hierarchical features for the face manipulation detection task.

Results and Implications

The experiments show that the proposed method achieves a notable advancement in detection accuracy across various video manipulations when compared to existing models. These findings underscore the importance of temporal modeling in detecting manipulated media, highlighting the limitations of approaches that focus solely on still images.

By addressing temporal incongruities, this research opens pathways for more robust detection systems that are capable of handling not just fake videos but potentially other forms of synthetic media. Practically, these advancements have significant implications for media verification processes, digital forensics, and platforms aiming to combat digital misinformation.

Future Speculations

Looking ahead, the fusion of spatial and temporal features through advanced architectures could offer even more robust solutions for video manipulation detection. The deployment of such systems in real-time applications, such as social media monitoring, poses challenges related to computational efficiency and scalability, which future research should aim to mitigate. Additionally, expanding the dataset diversity in terms of cultural, lighting, and textural variations can further generalize these models to real-world scenarios more effectively.

In conclusion, the methodology and findings presented in this work contribute an important step forward in the ongoing effort to tackle manipulation in digital content, particularly in video formats. The incorporation of temporal coherence analysis through recurrent convolutional strategies sets a precedent for future developments in this critical area of digital trust and security.