Ownership Authentication and Integrity Verification of Digital Images Using Generative Models and Custom Signature

In this paper we present an alternative distortion-free algorithm to ownership authentication and integrity veri-fication of digital images that use Contrastive Language-Image Pre-Training (CLIP), Vector Quantized Generative Adversarial Network (VQGAN) and a custom signature based on crypto-graphic message digest. Authentication and integrity of digital media are evaluated by the naked eye, as shown in the output of the generative architecture, as well as numerical metrics such as PSNR and SSIM, respectively. The code is publicly available at https://github.com/JonathanFlores250/NoiseHashGAN.