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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740429
• 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.

自动曝光虚假图片和视频的势头越来越大。随着视觉资产的指数级增长和越来越复杂的内容编辑工具，确实需要开发框架来支持媒体内容的监管链，以保持其证明价值。揭露图像伪造仍然非常具有挑战性。在文献中，基于足迹的主动和被动篡改方法最为普遍。主动形式通常分为两个阶段，首先在采集或外部共享之前在图像中插入一个片段（水印/签名）。在第二阶段，一个反向过程允许将原始代码和嵌入的代码片段并置并比对。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，即被篡改的图像可能包含可测量的操纵痕迹。用于解决双因素问题的双线性模型被广泛应用于不同领域，例如在黑白图像中分离风格和内容（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处理不同的因素（例如，面部表情与身份、头部姿势和照明的解耦）。在本研究中，我试图设计变分自编码器（VAE）模型来解决图像组成因素解耦的难题。不同的噪声、伪像和马赛克风格可以通过特定的基于对抗性架构的模型反向恢复，该模型致力于在成对和未成对图像之间进行风格转移。这些模型很好地归纳了样式分离的潜在代码。我的动机是VAE模型在一个高阶概率图形框架内提供了一个神经网络架构。我的目标是使用统一的框架直接从图像中学习潜在表示，旨在从被篡改的图像中分离出调质轮廓。我的假设是，在调质区域周围的操作痕迹（例如，局部噪声分布，伪影，前景/背景，场景照明等）与图像的其余部分不同。就应用程序而言，建议的框架可以在个人、组织和社会层面上找到用途。作为对可信但虚构的媒体的免疫剂，它可以被武装部队用来打击任何削弱军队道德的宣传，也可以被政府用来及时回应国内大规模的虚假信息（例如，实时揭开面具，以便在最后一刻对诽谤竞选活动做出即时回应）。它也可以被新闻媒体用来在任何大规模的公共传播之前核实资产的完整性。在法庭上，这种适用的解决办法建议采用数字确证证据（例如，经核实的监控录像）。它甚至会给证据提供决定性的确定性。