Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics

TL;DR

This paper investigates how artificial image tampering, such as morphing and deepfakes, distorts texture landmarks and quality features, and proposes using these distortions for explainable detection methods with low error rates.

Contribution

It extends previous work by demonstrating that tampering distorts the spatial distribution of texture landmarks and quality features, enabling effective detection on constrained devices.

Findings

Distortion of texture landmarks and quality features indicates tampering.

Proposed handcrafted detectors achieve low error rates.

Method suitable for implementation on resource-limited devices.

Abstract

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.