Enhancing Camera Source Identification: A Rapid Algorithm with Enhanced Discriminative Power
Source abstract
Abstract
Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search algorithm is crucial for the effective implementation of camera source identification. Despite its importance, this domain has witnessed limited research, with existing studies predominantly focusing on search efficiency while neglecting robustness, which is essential. In practical scenarios, query images often suffer from poor signal quality due to noise, and the variability in fingerprint quality across different sources presents a significant challenge. Conventional brute-force search algorithms (BFSAs) prove largely ineffective under these conditions because they lack the necessary robustness. This paper addresses the issues in digital image source identification by proposing a rapid fingerprint search algorithm based on global information. The algorithm innovatively introduces a search priority queue (SPQ), which analyzes the global correlation between the query fingerprint and all reference fingerprints in the database to construct a comprehensive priority ranking, thereby achieving the efficient retrieval of matching fingerprints. Compared to the traditional brute-force search algorithm (BFSA), our method significantly reduces computational complexity in large-scale databases, optimizing from O(nN) to O(nlogN), where n is the length of the fingerprint, and N is the number of fingerprints in the database. Additionally, the algorithm demonstrates strong robustness to noise, maintaining a high matching accuracy rate even when image quality is poor and noise interference is significant. Experimental results show that in a database containing fingerprints from 70 cameras, our algorithm is 50% faster in average search time than BFSA, and its matching accuracy rate exceeds 90% under various noise levels. This method not only improves the efficiency and accuracy of digital image source identification but also provides strong technical support for handling large-scale image data, with broad application prospects in fields such as copyright protection and forensic evidence.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Advanced Steganography and Watermarking Techniques · Generative Adversarial Networks and Image Synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Immigration Administration (Guangzhou), China People’s Police University, Guangzhou 510663, China
School of Automation, Guangdong University and Technology, Guangzhou 510006, China
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Funding
China People’s Police University · Hebei Education Department
References (40)
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