Image Fakery Detection: Towards a Trace Disentangling and Image Deconstruction Approaches

图像伪造检测：走向痕迹解缠和图像解构方法

• 批准号: RGPIN-2020-05171
• 负责人: Dahmane, Mohamed
• 金额: $ 1.75万
• 依托单位: Centre de recherche informatique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764484
• 关键词: Image; Fakery; Detection; Towards; Trace

The automation of exposing fake images and videos is gaining more and more momentum. With the exponential growth of visual assets and the more and more sophisticated tools of content editing, there is a real need of developing frameworks to support the chain of custody of media content to preserve its probative value. Unmasking image forgery remains very challenging. In the literature, the active and passive tampering footprints-based methods are the most prevalent. The active forms are mostly two-phases, first they insert a snippet (watermark/signature) in the image during acquisition or before external sharing. In a second phase, a reverse process permits to juxtapose and collate together the origin and the embedded snippet. On the contrary, the passive methods do not need any add-on information. They merely rely on the fact that a doctored image may contain measurable traces of manipulation. Bi-linear models for solving two-factor problems were widely adopted in different areas, such as separating style and content in white and black images (e.g., disentangle alphabet shape from calligraphy styles). Natural images need more complex multilinear models to cope with different factors (e.g., decoupling facial expressions from identity, head pose, and illumination). In this research, I'm seeking to design variational autroencoder (VAE) models to deal with the difficult problem of decoupling the constituent factors of the image. The different noise, artifact and mosaicking styles can be reversely recovered by particular adversarial architecture-based models, which are dedicated to style transferring between paired and unpaired images. These models are good for inducing latent codes for style separation. My motivation is that VAE models offer a neural net architecture within a high-order probabilistic graphical framework. My objective, is to use the unified framework to learn a latent representation directly from the image, aiming to disentangle the tempering profiles from doctored images. My assumption is that the traces of manipulation (e.g., local noise distribution, artifacts, foreground/backgrounds, scene lighting etc.) around the tempered region are different from those of the rest of the image. In terms of applications, the proposed framework could find uses at either individual, organizational, and societal levels. As an immunizer against credible yet fictitious media, it can be used by armed forces for combating any propaganda for sapping the troops' moral, by government to timely respond to domestic massive disinformation (e.g., real-time unmasking for an instant reply to a last-minute defamatory election campaign). It can be also used by the news media to verify the integrity of the assets before any mass public spreading. In courtroom, such applicable solutions would suggest digital corroborating evidence (e.g., verified surveillance video). It would even give a decisional certainty to the proof.

曝光虚假图像和视频的自动化正在获得越来越多的动力。随着视觉资产的指数增长和越来越复杂的内容编辑工具，存在开发框架以支持媒体内容的监管链以保持其证明价值的真实的需求。揭露图像伪造仍然非常具有挑战性。在文献中，基于主动和被动篡改足迹的方法是最流行的。活动形式主要是两个阶段，首先在获取期间或外部共享之前在图像中插入片段（水印/签名）。在第二阶段中，反向过程允许将源代码和嵌入的片段并置和整理在一起。相反，被动方法不需要任何附加信息。他们仅仅依赖于这样一个事实，即篡改的图像可能包含可测量的操纵痕迹。解决双因素问题的双线性模型在不同的领域被广泛采用，例如分离白色和黑色图像中的风格和内容（例如，将字母形状从书法风格中分离出来）。自然图像需要更复杂的多线性模型来科普不同的因素（例如，将面部表情与身份、头部姿势和照明解耦）。在这项研究中，我正在寻求设计变分自动编码器（VAE）模型来处理解耦图像的组成因素的困难问题。不同的噪声，伪影和马赛克风格可以通过特定的基于对抗架构的模型来恢复，该模型专用于在成对和非成对图像之间进行风格转换。这些模型有利于归纳出风格分离的潜码。我的动机是，VAE模型提供了一个高阶概率图形框架内的神经网络架构。我的目标，是使用统一的框架，直接从图像中学习一个潜在的表示，旨在从篡改的图像中解开回火配置文件。我的假设是，操纵的痕迹（例如，局部噪声分布、伪影、前景/背景、场景照明等）调和区域周围的区域与图像其余部分的区域不同。在应用方面，拟议的框架可以在个人、组织和社会层面找到用途。作为对可信但虚构的媒体的免疫力，它可以被武装部队用来打击任何削弱部队道德的宣传，被政府用来及时应对国内大规模虚假信息（例如，实时揭露，以便即时回复最后一分钟的诽谤性竞选活动）。它也可以被新闻媒体用来在任何大规模公开传播之前验证资产的完整性。在法庭上，这种适用的解决方案将建议数字确证证据（例如，监控录像）。它甚至会给证明一个决定性的确定性。