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Advances in AI based computer vision has led to a significant growth insynthetic image generation and artificial image tampering with seriousimplications for unethical exploitations that undermine person identificationand could make render AI predictions less explainable.Morphing, Deepfake andother artificial generation of face photographs undermine the reliability offace biometrics authentication using different electronic ID documents.Morphedface photographs on e-passports can fool automated border control systems andhuman guards.This paper extends our previous work on using the persistenthomology (PH) of texture landmarks to detect morphing attacks.We demonstratethat artificial image tampering distorts the spatial distribution of texturelandmarks (i.e. their PH) as well as that of a set of image qualitycharacteristics.We shall demonstrate that the tamper caused distortion of thesetwo slim feature vectors provide significant potentials for buildingexplainable (Handcrafted) tamper detectors with low error rates and suitablefor implementation on constrained devices.

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