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

SaTC：核心：中：基于传感器捕获模型的隐写术：用基于模型的设计代替启发式方法

• 批准号: 2028119
• 负责人: Jessica Fridrich
• 金额: $ 76.9万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028119&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Steganography; Based

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.

隐写术是一种秘密通信方法，其中实际的消息被隐藏在一些其他无害的外观对象中。数字媒体，如图像，是理想的，因为它们可以被稍微修改以编码秘密，而不会使这些修改对人类可见或被计算机检测到。因此，隐写术为那些审查通信渠道或禁止使用加密的国家的公民提供了隐私。该项目研究了这种隐形通信的基本原理和局限性，从详细的数学理解开始，了解数字图像是如何在相机内形成的。这提供了数学保证，隐藏的数据不能被对手发现，从而保护用户。该模型还有望在数字取证的相关领域中找到应用，该领域试图识别数字图像的哪一部分被更改，什么样的相机拍摄了图像，以及它受到了什么类型的增强。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在历史上依赖于启发式推理和安全的经验证据，这是由于与覆盖介质相关联的复杂性和缺乏易处理的模型。这使得它很难，如果可能的话，建立最优性，建立正式的安全性，并评估威胁。该项目的主要目标是从启发式方法转向基于紧密拟合模型的方法，以构建可证明安全的嵌入方案并正式评估其安全性。调查从一个域开始，在这个域中存在具有易于处理的数学描述的真正随机性，然后将其传播到实际数据隐藏发生的表示。如果开发（处理）的复杂性允许，这将通过采用RAW传感器捕获的像素特定模型并在嵌入域中以封闭形式导出模型来实现，或者通过使用高斯马尔可夫随机场、参数多变量模型、蒙特-卡罗采样和使用数据驱动模型导出（估计）紧密拟合模型来实现。多个密切相关的任务将进行调查，涉及推理的基础上统计假设检验和信息理论的隐写系统的设计，评估和分析提供了基础，并建立在一个给定的水平的统计检测能力的隐蔽通道。该项目有可能提高理论上的理解的复杂的用户之间的相互作用的隐蔽工具和对手的有效载荷缩放法律，界限上的安全隐蔽通信速率，和统计检测的隐蔽通信信道。利用数字媒体中不确定组件的紧密拟合模型将有助于消除数字媒体隐写术中经常依赖的技巧和直觉。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中找到应用，以确定出处、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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

STATISTICAL MODELS FOR IMAGE STEGANOGRAPHY EXPLAINING AND REPLACING HEURISTICS

解释和替换启发法的图像隐写统计模型

• 发表时间: 2021
• 期刊: Binghamton University magazine
• 作者: Butor, Jan

JPEG Compatibility Attack Revisited

重新审视 JPEG 兼容性攻击

• 发表时间: 2021
• 期刊: IEEE transactions on information forensics and security
• 影响因子: 6.8
• 作者: Dworetzky, Eli;Fridrich, Jessica
• 通讯作者: Fridrich, Jessica

Cost polarization by dequantizing for JPEG steganography

• DOI: 10.2352/ei.2023.35.4.mwsf-374
• 发表时间: 2023-01
• 作者: Edgar Kaziakhmedov;Yassine Yousfi;Eli Dworetzky;J. Fridrich

Data Augmentation for JPEG Steganalysis

• DOI: 10.1109/wifs53200.2021.9648390
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Workshop on Information Forensics and Security (WIFS)
• 作者: T. Itzhaki;Yassine Yousfi;J. Fridrich

Detector-Informed Batch Steganography and Pooled Steganalysis

• DOI: 10.1145/3531536.3532951
• 发表时间: 2022-06
• 期刊: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security
• 作者: Yassine Yousfi;Eli Dworetzky;J. Fridrich