In this paper, a new image hashing scheme satisfying robustness and security is proposed. We exploit the property of dimensionality reduction inherent in compressive sensing/sampling (CS) for image hash design. The gained benefits include (1) the hash size can be kept small and (2) the CS-based hash is computationally secure. We study the use of visual information fidelity (VIF) for hash comparison under Stirmark attacks. We further derive the relationships between the hash of an image and both of its MSE distortion and visual quality measured by VIF, respectively. Hence, based on hash comparisons, both the distortion and visual quality of a query image can be approximately estimated without accessing its original version. We also derive the minimum distortion for manipulating an image to be unauthentic to measure the security of our scheme. Index Terms-Authentication, Compressive sensing, Image hashing, Robustness, Security, and Visual information fidelity. A media hash is a content-based compact representation of a digital multimedia signal, which has been applicable extensively. Usually, an image hashing scheme should meet the following five requirements , an image authentication scheme based on CS and distributed source coding (DSC) was proposed, where the image hash is derived from the DSC-encoded quantized random projection coefficients of an image. To perform authentication, a DSC decoder decodes the received hash bits with the test image serving as the side information, where the authenticity depends on the success/fail of the DSC decoding. A similar scheme was also proposed in [5] . In this paper, a robust and secure image hashing scheme via CS and visual information fidelity (VIF) is proposed, which can meet the above-mentioned five requirements. The novelties and advantages from exploiting CS and VIF of our scheme can be summarized as follows: (1) low-complexity hash extraction: the hash can be simultaneously extracted while acquiring an image using the CS single-pixel imaging camera [9] and computational security can be guaranteed without performing additional randomization process; (2) short hash length: coming from the highly sparse signal dimensionality reduction capability of CS; (3) advanced hash comparison: the highly sparse signal reconstruction capability for hash comparison; (4) perceptual robustness: the hash comparison via VIF is robust against attacks; and (5) the estimations for both image distortion and visual quality via hash comparisons. The major differences distinguishing our scheme from [4]-[5] include: (1) both the random projection and reconstruction capabilities for sparse signal of CS are exploited in
In REF , dimensionality reduction inherent in compressive sensing is exploited via random projection for image hash design.
Compressive sensing-based image hashing
2009 16th IEEE International Conference on Image Processing (ICIP)
2009 16th IEEE International Conference on Image Processing (ICIP)
Computer Science