Erasing the Shadow: Sanitization of Images with Malicious Payloads Using Deep Autoencoders

Steganography is used by threat actors to avoid detection or bypass blockages. Among the various approaches, hiding data within digital images is now the preferred offensive technique. Alas, developing attack-agnostic mitigation mechanisms is difficult, especially due to the tight relation between the images and the steganographic approach. Therefore, this paper takes advantage of autoencoders for sanitization, i.e., to disrupt the malicious information hidden in images without altering the visual quality. To this aim, we used an enhanced U-Net-like neural architecture. Results obtained with realistic threats showcased that our approach can effectively disrupt cloaked data and prevent the recovery of the payload while preserving the original image quality.