SaTC: CORE: Medium: Steganography Based on Sensor Capture Models: Replacing Heuristics with Model-Based Designs

隐写术是一种秘密通信方法，在这种方法中，实际信息隐藏在其他看起来无害的物体中。数字媒体(如图像)非常适合于此目的，因为可以对它们进行轻微修改以对秘密进行编码，而不会使这些修改对人类可见或计算机可检测。因此，在审查通信渠道或禁止使用加密的国家，隐写术为公民提供了隐私。这个项目研究这种隐形通信的基本原理和限制，首先从详细的数学理解开始，了解数码图像是如何在相机内形成的。这在数学上保证了隐藏的数据不会被对手发现，从而保护了用户。该模型还有望在数字取证的相关领域找到应用，该领域试图识别数字图像的哪一部分被更改，使用哪种相机拍摄的图像，以及对其进行了哪种类型的增强。该项目与国土安全、入侵检测及其预防、信息保障和可信信息交换密切相关。数字媒体中的隐写术历来依赖于启发式推理和安全的经验证据，这是因为与封面媒体相关的复杂性和缺乏易于处理的模型。这使得在可能的情况下，很难建立最优状态、正式建立安全和评估威胁。该项目的主旨是从启发式方法转向基于紧匹配模型的方法，以构造可证明安全的嵌入方案并对其安全性进行形式化评估。调查开始于其中存在具有易于处理的数学描述的真正随机性的域，然后将其传播到发生实际数据隐藏的表示。这将通过对原始传感器捕获采用像素特定的模型，并在开发(处理)的复杂性允许的情况下在嵌入域中以封闭形式导出模型，或者通过使用高斯马尔可夫随机场、参数多变量模型、蒙特卡罗抽样和使用数据驱动模型来导出(估计)紧密匹配的模型来实现。将调查多个密切相关的任务，这些任务涉及基于统计假设检验和信息论的推理，以提供设计、评估和分析隐写系统的基础，并在给定的统计可检测性水平上建立隐蔽通道的能力。该项目有可能在理论上改善对秘密工具用户与对手之间在有效载荷比例法则、安全秘密通信速率的界限以及秘密通信渠道的统计可检测性方面的复杂交互作用的理解。利用数字媒体中不确定成分的紧密匹配模型，将有助于消除数字媒体隐写术中经常依赖的启发式和直觉。像素特定的传感器捕获模型将在数字媒体取证的相关领域找到应用，以确定来源、来源和完整性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Steganography is a secret communication method in which the actual message is hidden in some other innocuous looking object. Digital media, such as images, are ideal for this purpose because they can be slightly modified to encode a secret without making these modifications visible to a human being or detectable by a computer. Steganography thus offers privacy to citizens in countries that censor communication channels or prohibit the usage of encryption. This project studies the fundamental principles and limits of such stealth communication by starting with a detailed mathematical understanding of how a digital image is formed inside the camera. This provides mathematical guarantee that the hidden data cannot be discovered by an adversary, protecting thus the users. The model is also expected to find applications in a related field of digital forensics that tries to identify which portion of a digital image has been altered, what kind of camera took the image, and what type of enhancement it was subjected to. This project is closely related to homeland security, intrusion detection and its prevention, information assurance, and trusted information exchange. Steganography in digital media has historically relied on heuristic reasoning and empirical evidence of security due to the complexity associated with the cover medium and the lack of tractable models. This makes it difficult, if possible, to establish optimality, establish security formally, and assess threats. The main thrust of this project is to move from heuristics to approaches that are based on tight-fitting models to construct provably secure embedding schemes and assess their security formally. The investigation begins with a domain in which true randomness with a tractable mathematical description exists, which is then propagated to the representation in which the actual data hiding occurs. This will be achieved by adopting pixel-specific models for the RAW sensor capture and deriving a model in the embedding domain in a closed- form, if the complexity of the development (processing) allows, or by deriving (estimating) a tightly-fitting model using Gaussian Markov random fields, parametric multivariate models, Monte-Carlo sampling, and using data-driven models. Multiple closely related tasks will be investigated that involve reasoning based on statistical hypothesis testing and information theory to provide the foundation for design, assessment, and analysis of steganographic systems, and to establish capacity of covert channels at a given level of statistical detectability. The project has the potential to improve theoretical understanding of the complex interaction between the users of covert tools and the adversaries in terms of payload scaling laws, bounds on secure covert communication rates, and on statistical detectability of the covert communication channel. Leveraging tightly fitting models of indeterministic components within digital media will help remove heuristics and intuition so often relied upon in digital-media steganography. Pixel-specific sensor capture models will find applications in the related field of digital media forensics for determining provenance, origin, and integrity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.