FastForensics: Efficient Two-Stream Design for Real-Time Image Manipulation Detection


zhangyangxiang (Ocean University of China), Yuezun Li (Ocean University of China), Ao Luo (Southwest Jiaotong University), Jiaran Zhou (Ocean University of China), Junyu Dong (Ocean University of China)
The 35th British Machine Vision Conference
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Abstract

With the rise in popularity of portable devices, the spread of falsified media on social platforms has become rampant. This necessitates the timely identification of authentic content. However, most advanced detection methods are computationally heavy, hindering their real-time application. In this paper, we describe an efficient two-stream architecture for real-time image manipulation detection. Our method consists of two-stream branches targeting the cognitive and inspective perspectives. In the cognitive branch, we propose efficient wavelet-guided Transformer blocks to capture the global manipulation traces related to frequency. This block contains an interactive wavelet-guided self-attention module that integrates wavelet transformation with efficient attention design, interacting with the knowledge from the inspective branch. The inspective branch consists of simple convolutions that capture fine-grained traces and interact bidirectionally with Transformer blocks to provide mutual support. Our method is lightweight (∼ 8M) but achieves competitive performance compared to many other counterparts, demonstrating its efficacy in image manipulation detection and its potential for portable integration.

Citation

@inproceedings{zhangyangxiang_2024_BMVC,
author    = {zhangyangxiang and Yuezun Li and Ao Luo and Jiaran Zhou and Junyu Dong},
title     = {FastForensics: Efficient Two-Stream Design for Real-Time Image Manipulation Detection},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0339.pdf}
}


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