CIF: Small: End to End Security-Oriented Optimization of Image Acquisition Pipelines

CIF：小型：图像采集管道的端到端安全优化

• 批准号: 1909488
• 负责人: Nasir Memon
• 金额: $ 49.78万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909488&HistoricalAwards=false
• 关键词: CIF; Small; End; Security; Oriented

Ensuring integrity of digital images is one of the most challenging problems of our times. Increasing capabilities of digital media editing software has spawned a torrent of shocking manipulation examples, including the infamous DeepFakes which has been deemed a looming challenge for privacy, democracy, and national security. Many solutions proposed to-date have fallen short and can be rendered ineffective with very simple post-processing. In particular, strong compression applied by social networks and photo sharing services render existing authentication protocols unreliable. Moreover, increasing adoption of deep learning and computational photography in imaging processors in digital cameras creates new challenges even in native photo authentication. This project will use modern machine learning techniques to pursue security-oriented design of image acquisition and distribution workflows to ensure that image integrity and provenance can be assured, thereby addressing an emerging problem in this social-media driven world. This project seeks to tackle: 1) optimization of the imaging pipeline to facilitate reliable forensic analysis in the most challenging conditions; 2) design of training protocols that generalize to various authentication problems; 3) optimization of the entire acquisition and distribution workflow, including both the imaging processor and lossy compression. Preliminary experiments indicate that by exploiting feedback from post-distribution forensic analysis, the imaging pipeline can be modified to facilitate content authentication. A neural imaging pipeline can learn to introduce imperceptible artifacts, akin to digital watermarks, which significantly increase manipulation detection accuracy, from 45% to over 90%. Further gains will result from the explorations on this project along the thrusts noted above.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.

确保数字图像的完整性是我们这个时代最具挑战性的问题之一。数字媒体编辑软件的功能不断增强，催生了一系列令人震惊的操纵案例，包括臭名昭著的DeepFakes，它被认为是对隐私、民主和国家安全的迫在眉睫的挑战。迄今为止提出的许多解决方案都不够，并且可以通过非常简单的后处理变得无效。特别地，由社交网络和照片共享服务应用的强压缩使得现有认证协议不可靠。此外，在数码相机的成像处理器中越来越多地采用深度学习和计算摄影，即使在原生照片认证中也会带来新的挑战。该项目将使用现代机器学习技术来追求图像采集和分发工作流程的安全导向设计，以确保图像的完整性和出处，从而解决这个社交媒体驱动的世界中出现的问题。该项目旨在解决：1）优化成像管道，以便于在最具挑战性的条件下进行可靠的法医分析; 2）设计适用于各种认证问题的训练协议; 3）优化整个采集和分发工作流程，包括成像处理器和有损压缩。初步实验表明，通过利用来自分发后取证分析的反馈，可以修改成像流水线以促进内容认证。神经成像管道可以学习引入不可感知的伪影，类似于数字水印，这将显著提高操作检测的准确性，从45%提高到90%以上。进一步的收益将导致对这个项目的探索沿着上述的推力.这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准.