Digital Forensics Investigation for Attacks on Artificial Intelligence

Abstract

The new research approaches are needed to be adopted to deal with security threats in Artificial Intelligence (AI)-based systems. This research is aimed at investigating the AI attacks that are “malicious by design.” It also deals with conceptualization of the problem and strategies for attacks on AI using digital forensic tools. A specific class of problems in adversarial attacks are tampering of images for computational processing in applications of digital photography, computer vision, pattern recognition (facial capping algorithms). State-of-the-art developments in forensics, such as 1. Application of end-to-end Neural Network training pipeline for image rendering and provenance analysis. 2. Deep fake image analysis using frequency methods, wavelet analysis, and tools like Amped Authenticate. 3. Capsule networks for detecting forged images. 4. Information transformation for feature extraction via image forensic tools, such as EXIF-SC, Splice Radar, and Noiseprint. 5. Application of generative adversarial networks (GAN) based models as anti-image forensics [8], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of digital forensics.