An Auto-Encoder Based Method for Camera Fingerprint Compression

Camera fingerprint links a picture to its camera sensor, which is widely applied in sensor device identification, social network tracing and forgery detection. However, such fingerprints are in high dimensionality and cost substantial memory and computing resources, limiting their uses in real-time processing on embedded devices. In this paper, we introduce a new method to compress high-dimensional floating-point fingerprints to low-dimensional binary features to save storage as well as maintaining their representative abilities. Also, we present a much faster approach to sensor device matching with hamming distance, compared with the commonly used Peak to Correlation Energy (PCE) distance. Our method contains two stages. First, raw fingerprints are compressed into low-dimensional features with our proposed grouping strategy and auto-encoder based model. Then, the compressed floating-point features are further converted into more compact binary features. Experiments show that our method achieves superior performance over several competitive compression methods in both identification and verification tasks.