2019/10/25 by Panagiotis Linardos, Linardos, Panagiotis, Suzanne Little +3
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.1910.11603
openalex publication_date 2019/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work tackles the Pixel Privacy task put forth by MediaEval 2019. Our goal is to manipulate images in a way that conceals them from automatic scene classifiers while preserving the original image quality. We use the fast gradient sign method, which normally has a corrupting influence on image appeal, and devise two methods to minimize the damage. The first approach uses a map of pixel locations that are either salient or flat, and directs perturbations away from them. The second approach subtracts the gradient of an aesthetics evaluation model from the gradient of the attack model to guide the perturbations towards a direction that preserves appeal. We make our code available at: https://git.io/JesXr.