Passive steganalysis based on higher order image statistics of curvelet transform
International Journal of Automation and Computing
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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This paper proposes a reliable framework for the detection of the Least Significant Bit (LSB) steganography using digital media files as cover objects. Steganographic methods attempt to insert datain multimedia signals in an undetectable fashion. However, these methods often disrupt the underlying signal characteristics, thereby allowing detection under careful steganalysis. Under repeated embedding, disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. This principle is used to derive a steganalysis tool that detects the presence of hidden messages in uncompressed twenty-four bits BMP image. This work presents Close Color Pair Analysis with Stego-Sensitive Threshold (CCPASST) to detect stego-objects with even 10% payload. In earlier works 20% payload wasdetected through Close Color Pair Analysis. The new framework exploits the First-Order Statistics of Structural Similarity Index Measure of the samples to calculate the threshold. The literature contains only one other detector specialized with variable threshold, and the one presented here is substantially more sensitive. Simulation results with the stego-sensitive threshold applied on well-known LSB steganographic technique indicate that this approach is superior to the earlier methods and is able with promising accuracy to distinguish between clean and stego images.