Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. In this paper, we provide a comprehensive review of the current media tampering detection approaches, and discuss the challenges and trends in this field for future research.
翻译:在线媒体数据,以图像和视频形式出现的在线媒体数据,正在成为主流通信渠道。然而,最近深层次学习的进展,特别是深层的基因模型,以低成本为制作具有说服力的图像和视频打开了大门,不仅对数字信息的信誉构成严重威胁,而且具有严重的社会影响。这促使人们日益关注对媒体篡改检测的研究,即利用深层次的学习技术来检查媒体数据是否被恶意操纵。根据目标图像的内容,媒体伪造可分为图像篡改和深藏技术。前者通常会移动或抹除普通图像中的视觉元素,而后者则操纵人类面孔的表达方式,甚至身份。因此,国防手段包括图像篡改检测和深藏探测,这具有广泛的特性。在本文件中,我们全面审查了当前媒体篡改检测方法的情况,并讨论了该领域的挑战和趋势,供今后研究。