We present a novel technique for effective steganalysis of high-color-depth digital images that have been subjected to embedding by LSB steganographic algorithms. The detection theory is based on the idea that under repeated embedding, the disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. That is the marginal distortions due to repeated embeddings decrease monotonically. This decreasing distortion property exploited with Close Color Pair signature is used to construct the classifier that can distinguish between stego and cover images. For evaluation, a database composed of 1200 plain and stego images (at 10% and 20% payload and each one artificially adulterated with 20% additional data) was established. Based on this database, extensive experiments were conducted to prove the feasibility of our proposed system.

Our main results are (i) a 90%+ positive-detection rate; (ii) Close Color Pair ratio is not modified significantly when additional bit streams are embedded into a test image that is already tampered with a message.; (iii) an image quality metric Czenakowski Measure, that is substantially sensitive to LSB embedding is utilized to derive the effective image adaptive threshold; (iv) capable of detecting stego images with an embedding of even 10% payload while the earlier methods can achieve the same detection rate only with 20% payload.