ArticleEntropy (Basel, Switzerland)2022
Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images.PeerJ. Computer science · 2024Article
- Neural noiseprint transfer: a generic noiseprint-based counter forensics framework.PeerJ. Computer science · 2023Article
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Authors and funding
2 authors.
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Abstract
Camera sensor identification can have numerous forensics and authentication applications. In this work, we follow an identification methodology for smartphone camera sensors using properties of the Dark Signal Nonuniformity (DSNU) in the collected images. This requires taking dark pictures, which the users can easily do by keeping the phone against their palm, and has already been proposed by various works. From such pictures, we extract low and mid frequency AC coefficients from the DCT (Discrete Cosine Transform) and classify the data with the help of machine learning techniques. Traditional algorithms such as KNN (K-Nearest Neighbor) give reasonable results in the classification, but we obtain the best results with a wide neural network, which, despite its simplicity, surpassed even a more complex network architecture that we tried. Our analysis showed that the blue channel provided the best separation, which is in contrast to previous works that have recommended the green channel for its higher encoding power.
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