Pixel Tampering Detection in Encrypted Surveillance Videos on Resource-Constrained Devices

Abstract

Encryption (naïve/selective) is recommended to secure the recorded visual content; however, intruders can still manipulate encrypted data. Visually, tampering attacks on encrypted video pixels in selectively encrypted videos are difficult to identify. Thus, this paper presents a tampering detection system that performs vulnerability analysis for Regions-of-Interest (ROI) in encrypted videos. To detect the tampering attacks, we explored the pixels' intensities and proposed a new TampDetect algorithm. The TampDetect applies the Hue, Saturation, and Value (HSV) colour model to detect the encrypted areas in the video. These encrypted pixels are then segmented and selected from the non-encrypted pixels using a minimum and maximum HSV value and a global threshold. The mean intensity of the encrypted pixels is thus calculated and stored. The integrity of the video frame is then validated by comparing the stored mean intensity with the newly calculated mean intensity to validate tampering/attack. The experiments were conducted on an Intel NUC, and its low computational cost demonstrates the lightweight nature of the proposed TampDetect algorithm for detecting tampering in ROI encrypted videos. The developed dataset for experiments, i.e., original, ROI encrypted, and tampered videos (encrypted/decrypted) is made available on kaggle-repository for future researchers.