做还是不做Beta：数字图像取证中的信息瓶颈
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
AI总结：
本文提出InfoPrint，利用信息瓶颈和近似变分推断定位图像篡改区域，在标准数据集上超越现有方法，并能检测GAN修复的修改。
摘要：
我们考虑采用信息论方法来解决识别伪造数字图像的问题。我们提出了一种创新方法，将图像中篡改区域的定位问题表述为使用信息瓶颈（IB）的深度表示学习问题，信息瓶颈最近作为解释深度神经网络的框架而受到欢迎。篡改图像构成了严重的困境，因为数字化媒体是我们生活中无处不在的一部分。图像编辑软件的易得性促进了这些篡改行为，而生成对抗网络（GANs）等深度生成模型的最新进展则加剧了这一问题。我们提出了InfoPrint，一种使用近似变分推断的IB公式的计算高效解决方案，并将其与计算成本高昂的数值解进行比较。在多个标准数据集上的测试表明，InfoPrint优于最先进的方法和数值解。此外，它还具有检测由修复GANs进行的修改的能力。
英文摘要：
We consider an information theoretic approach to address the problem of identifying fake digital images. We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks. Tampered images pose a serious predicament since digitized media is a ubiquitous part of our lives. These are facilitated by the easy availability of image editing software and aggravated by recent advances in deep generative models such as GANs. We propose InfoPrint, a computationally efficient solution to the IB formulation using approximate variational inference and compare it to a numerical solution that is computationally expensive. Testing on a number of standard datasets, we demonstrate that InfoPrint outperforms the state-of-the-art and the numerical solution. Additionally, it also has the ability to detect alterations made by inpainting GANs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
