Content-Aware Tunable Selective Encryption for HEVC Using Sine-Modular Chaotification Model

Abstract

Existing High Efficiency Video Coding (HEVC) selective encryption algorithms only consider the encoding characteristics of syntax elements to keep format compliance, but ignore the semantic features of video content, which may lead to unnecessary computational and bit rate costs. To tackle this problem, we present a content-aware tunable selective encryption (CATSE) scheme for HEVC. First, a deep hashing network is adopted to retrieve groups of pictures (GOPs) containing sensitive objects. Then, the retrieved sensitive GOPs and the remaining insensitive ones are encrypted with different encryption strengths. For the former, multiple syntax elements are encrypted to ensure security, whereas for the latter, only a few bypass-coded syntax elements are encrypted to improve the encryption efficiency and reduce the bit rate overhead. The keystream sequence used is extracted from the time series of a new improved logistic map with complex dynamic behavior, which is generated by our proposed sine-modular chaotification model. Finally, a reversible steganography is applied to embed the flag bits of the GOP type into the encrypted bitstream, so that the decoder can distinguish the encrypted syntax elements that need to be decrypted in different GOPs. Experimental results indicate that the proposed HEVC CATSE scheme not only provides high encryption speed and low bit rate overhead, but also has superior encryption strength than other state-of-the-art HEVC selective encryption algorithms.