论文标题
人造图像篡改扭曲纹理地标和质量特征的空间分布
Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics
论文作者
论文摘要
Advances in AI based computer vision has led to a significant growth in synthetic image generation and artificial image tampering with serious implications for unethical exploitations that undermine person identification and could make render AI predictions less explainable.Morphing, Deepfake and other artificial generation of face photographs undermine the reliability of face biometrics authentication using different electronic ID documents.Morphed face photographs on e-passports can fool automated本文扩展了我们先前关于使用纹理地标的持续同源（pH）来检测变形攻击的工作。我们表明，人工图像篡改篡改纹理地标的空间分布（即其pH）的空间分布（即它们的pH）以及对这些图像质量的重要特征，我们将造成这些较大的特征。这两种特征的特征是the twefter defters vect for vect for the tam for的特征。 （手工制作的）错误率较低且适合在受限设备上实现的篡改探测器。
Advances in AI based computer vision has led to a significant growth in synthetic image generation and artificial image tampering with serious implications for unethical exploitations that undermine person identification and could make render AI predictions less explainable.Morphing, Deepfake and other artificial generation of face photographs undermine the reliability of face biometrics authentication using different electronic ID documents.Morphed face photographs on e-passports can fool automated border control systems and human guards.This paper extends our previous work on using the persistent homology (PH) of texture landmarks to detect morphing attacks.We demonstrate that artificial image tampering distorts the spatial distribution of texture landmarks (i.e. their PH) as well as that of a set of image quality characteristics.We shall demonstrate that the tamper caused distortion of these two slim feature vectors provide significant potentials for building explainable (Handcrafted) tamper detectors with low error rates and suitable for implementation on constrained devices.
