Perceptual video hashing in P2P networks

Content identification in peer-to-peer (P2P) networks has until now been achieved by using metadata or cryptographic hashes. However, with increasing number of duplicates in different names and formats especially in (unmanaged) P2P networks, these tools have become insufficient for proper content finding and access right management. A complementary possible approach is to identify the content in P2P networks by using perceptual hashes, (or fingerprints) extracted from the perceptual features of the content robust to typical processing. In this paper, we first discuss the essential differences in fingerprint size, fingerprint extraction complexity, and fingerprint search methodology for a video identification system in P2P networks compared with central database applications. Then we propose a novel method optimized for P2P networks that uses only differences of video frame means. The proposed method reduces the fingerprint sizes into kilobytes, extraction time to seconds, and search duration into milliseconds. Furthermore, the uniform distribution of the extracted fingerprints enables the usage of existing DHT-based keyword search methods for fingerprint queries.