Deep Learning and Interpretability in Digital Image Forensics

在新的AI时代(或者说相当于《假新闻》时代)，眼见为实将不再可信，甚至真相也不会被相信。确保数字图像的完整性和真实性越来越具有挑战性和重要性。有了先进的图像编辑工具和深度学习(DL)模型，人们可以轻松地操纵数字图像或生成视觉上非常令人信服的虚假图像和视频，包括臭名昭著的DeepFake，因此对数字图像取证(DIF)分析构成了关键挑战。DIF分析识别数字图像中细微的、感知上看不见的法医痕迹的存在、缺失或不一致，以验证其来源、完整性和真实性。深度学习已被广泛应用于DIF任务。不幸的是，数字图像和深度学习模型都容易受到有意或无意的操纵和攻击。敌意的例子是攻击者故意设计的，通过在干净的图像中添加特定的人类无法察觉的扰动，可以很容易地愚弄DL模型犯错误。尤其是考虑到当前数字图书馆模型的“黑箱”性质，数字图书馆的机遇伴随着与DIF相关的“巨大挑战”。DIF领域的性质和范围使深度学习的可解释性变得越来越重要。在这一愿景下，这一拟议的研究计划将专注于探索数字图像取证和深度学习的交叉，以最终确保信任图像并实现更多信任的DL解决方案。更具体地说，拟议的研究计划将追求以下主要技术目标：(A)探索数字图像取证中的深度学习可解释性：我们的目标是为传统图像取证问题和对抗性深度学习问题建立评估基准并开发新的可解释性模型；(B)为通用DIF分析开发包含解释的深度学习框架(例如，同时检测不同的图像操纵和攻击)；以及(C)开发对手深度学习方法以提供模型不可知的对抗性攻击和攻击不可知的对抗性防御。这项拟议的研究解决了数字图像安全和取证方面的根本挑战--数字图像和深度学习模型的脆弱性，并有可能在为信任图像实现更可信的DL解决方案方面做出有影响力的贡献。我们预期打击虚假内容和攻击的能力的提高将对社会、隐私和国家安全带来巨大好处。数字图书馆正在重塑许多行业，给许多研究领域带来革命性的变化，我们的研究将有助于实现信赖数字图书馆的完整愿景，再向前迈进一小步。该研究项目将为学生提供接受相关尖端技术培训的机会。

In the new AI era (or equivalently in the "Fake News" era), seeing will no longer be believing and even truth will not be believed. Ensuring integrity and authenticity of digital images is increasingly challenging and vital. With advanced image editing tools and deep learning (DL) models, people can easily manipulate digital images or generate highly visually convincing fake images and videos, including the infamous DeepFakes, and therefore pose critical challenges in digital image forensics (DIF) analysis. DIF analysis identifies the existence, lack or inconsistency of subtle, perceptually invisible forensic traces in a digital image to validate its origin, integrity and authenticity. Deep learning has been widely employed for DIF tasks. Unfortunately, both digital images and deep learning models are vulnerable to manipulations and attacks, intentionally or unintentionally. Adversarial examples, which an attacker has intentionally designed by adding specific human imperceptible perturbations into clean images, can easily fool a DL model to make a mistake. Particularly with the "black box" nature of current DL models, DL opportunities come with "big challenges" associated with DIF. The nature and scope of the DIF field has rendered deep learning interpretability increasingly critical. With this vision, this proposed research program will focus on exploring the intersection of digital image forensics and deep learning to eventually ensure trusting images and achieve more trusting DL solutions. More specifically, the proposed research program will pursue the following main technical objectives: (a) Exploring deep learning interpretability in digital image forensics: We aim to establish the evaluation benchmark and develop novel interpretability models for both conventional image forensics problems and adversarial deep learning problems; (b) Developing interpretation-incorporated deep learning frameworks for the general-purpose DIF analysis (e.g., simultaneous detection of different image manipulations and attacks); and (c) Developing adversary deep learning approaches to provide model-agnostic adversarial attacks and attack-agnostic adversarial defenses. The proposed research addresses fundamental challenges in digital image security and forensics -- the vulnerability of digital images and deep learning models, and has the potential to make influential contributions in achieving more trusting DL solutions for trusting images. Our expected improvements in the ability to combat fake contents and attacks will be of great benefit to society, privacy and national security. DL is reshaping many industries and revolutionizing many research fields, and our research will help achieve the full vision of trusting DL one tiny step further. The research program will provide an opportunity for the students to be trained in related cutting-edge technologies.