连续假媒体检测:让深度假货检测器适应新的生成技术

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Francesco Tassone , Luca Maiano , Irene Amerini
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引用次数: 0

摘要

在这些技术炒作的推动下,生成技术继续以惊人的速度发展。这种快速发展严重限制了深度假货检测器的应用,尽管科学界做出了许多努力,但这些检测器仍难以在不断变化的内容面前获得足够强大的性能。为了解决这些局限性,我们在本文中提出了对两种持续学习技术在短篇和长篇虚假媒体序列上的分析。这两个序列都包括来自 GAN、计算机图形技术和未知来源的复杂、异构的深度伪造(生成的图像和视频)。我们的实验表明,持续学习对于减少对通用性的需求非常重要。事实上,我们表明,尽管有一些局限性,但持续学习方法有助于在整个训练序列中保持良好的性能。不过,要使这些技术以足够稳健的方式发挥作用,序列中的任务必须具有相似性。事实上,根据我们的实验,任务的顺序和相似性会随着时间的推移影响模型的性能。为了解决这个问题,我们证明可以根据任务的相似性对其进行分组。即使在较长的序列中,这种小措施也能显著提高性能。这一结果表明,持续性技术可以与最有前途的检测方法相结合,从而赶上最新的生成技术。除此之外,我们还概述了如何将这种学习方法集成到持续集成和持续部署(CI/CD)的深度伪造检测管道中。这样,您就可以跟踪不同的基金,如社交网络、新生成工具或第三方数据集,并通过集成持续学习,对检测器进行持续维护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Continuous fake media detection: Adapting deepfake detectors to new generative techniques

Generative techniques continue to evolve at an impressively high rate, driven by the hype about these technologies. This rapid advancement severely limits the application of deepfake detectors, which, despite numerous efforts by the scientific community, struggle to achieve sufficiently robust performance against the ever-changing content. To address these limitations, in this paper, we propose an analysis of two continuous learning techniques on a Short and a Long sequence of fake media. Both sequences include a complex and heterogeneous range of deepfakes (generated images and videos) from GANs, computer graphics techniques, and unknown sources. Our experiments show that continual learning could be important in mitigating the need for generalizability. In fact, we show that, although with some limitations, continual learning methods help to maintain good performance across the entire training sequence. For these techniques to work in a sufficiently robust way, however, it is necessary that the tasks in the sequence share similarities. In fact, according to our experiments, the order and similarity of the tasks can affect the performance of the models over time. To address this problem, we show that it is possible to group tasks based on their similarity. This small measure allows for a significant improvement even in longer sequences. This result suggests that continual techniques can be combined with the most promising detection methods, allowing them to catch up with the latest generative techniques. In addition to this, we propose an overview of how this learning approach can be integrated into a deepfake detection pipeline for continuous integration and continuous deployment (CI/CD). This allows you to keep track of different funds, such as social networks, new generative tools, or third-party datasets, and through the integration of continuous learning, allows constant maintenance of the detectors.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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