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Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics
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.
Persistent Homology for Breast Tumor Classification using Mammogram Scans
An Important tool in the field topological data analysis is known as persistent Homology (PH) which is used to encode abstract representation of the homology of data at different resolutions in the form of persistence diagram (PD).
