Quantum-aware image encoding and adversarial perturbation

Abstract

The emergence of generative AI poses problems for people in several industries, including artists and other content creators whose work can be trained on without their consent, along with public figures who can be impersonated. As generative AI is now being researched at the quantum level, adversarial AI in quantum remains a largely unexplored topic. With present NISQ architecture and current hardware limitations for quantum image processing in mind, we present a hybrid quantum adversarial AI system using the QPIXL++ library for investigating image perturbations with quantum encoding using Flexible Representation of Quantum Images (FRQI) and Sparse Fast Walsh Hadamard Transform (SFWHT) compression. Topics for investigation in this area include pixel-domain vs frequency-domain perturbations under quantum compression, optimal perturbation strength, quantum superposition, working around quantum hardware limitations and applying optimization techniques with models as DINOv2. This project aims to establish a foundation and launch new research directions in hybrid quantum machine learning and adversarial AI. After completion of the study, it was found that the mean preserved perturbation value for the images was 99.99%, meaning that most of the classically introduced perturbations survived quantum encoding. Additionally, the mean SSIM vaule was close to 1 and the mean PSNR value was 32.30 dB which is above the accepted threshold for indistinguishable images to human eyes.