Robust Homomorphic Video Hashing

TL;DR

This paper introduces a scalable, robust video hashing method that can identify videos despite common manipulations, work on encrypted videos, and serve as a forensic tool to combat illegal content while preserving privacy.

Contribution

The proposed video hashing technique is novel in its robustness to manipulations and its ability to operate on encrypted videos without decryption.

Findings

Robustness to scaling, noise, compression, and contrast changes.

Effective in large-scale video matching scenarios.

Operates directly on encrypted videos for forensic analysis.

Abstract

The Internet has been weaponized to carry out cybercriminal activities at an unprecedented pace. The rising concerns for preserving the privacy of personal data while availing modern tools and technologies is alarming. End-to-end encrypted solutions are in demand for almost all commercial platforms. On one side, it seems imperative to provide such solutions and give people trust to reliably use these platforms. On the other side, this creates a huge opportunity to carry out unchecked cybercrimes. This paper proposes a robust video hashing technique, scalable and efficient in chalking out matches from an enormous bulk of videos floating on these commercial platforms. The video hash is validated to be robust to common manipulations like scaling, corruptions by noise, compression, and contrast changes that are most probable to happen during transmission. It can also be transformed into the encrypted domain and work on top of encrypted videos without deciphering. Thus, it can serve as a potential forensic tool that can trace the illegal sharing of videos without knowing the underlying content. Hence, it can help preserve privacy and combat cybercrimes such as revenge porn, hateful content, child abuse, or illegal material propagated in a video.