Perceptual Video Hashing With Secure Anti-Noise Model for Social Video Retrieval

Lv Chen, Dengpan Ye, Yueyun Shang

Abstract: In real scenarios, videos are usually corrupted by multiple types of noise, which brings great challenges to retrieving social videos. However, most of the current video hashing methods for video retrieval consider the attack of a single noise model, and rarely discuss when dealing with complex noise models, which is not conducive to solving the above difficulties. Thus, we describe a novel video hashing with secure anti-noise model (SANM). To improve the robustness of noise attacks, the input video is reconstructed into an SANM by low-rank representation (LRR) and random subspace partition (RSP). LRR is useful technique for capturing the global structure of data. It focuses on recovering the underlying subspace in noisy environment and helps to make the proposed model robust to multiple noises. In addition, using chaotic mapping to control the generation of RSP can ensure the security of proposed model. Then, a new subspace decomposition descriptor (SDD) is proposed. SDD is obtained by calculating the invariant distances of the factor matrices obtained by Tucker decomposition, and is used to decompose SANM to derive a compact hash. Various experiments demonstrate that the SANM hashing performs better than several state-of-the-art algorithms in terms of good robustness and discrimination, and it can accurately retrieve social videos.