Abstract. This paper presents an optimum histogram pair based image reversible data hiding scheme using integer wavelet transform and adaptive histogram modification. This new scheme is characterized by (1) the selection of best threshold T , which leads to the highest PSNR of marked image for a given payload; (2) the adaptive histogram modification, which aims at avoiding underflow and/or overflow, is carried out only when it is necessary, and treats the left side and right side of histogram individually, seeking a minimum amount of histogram modification; and (3) the selection of most suitable embedding region, which attempts to further improve the PSNR of marked image in particular when the payload is low. Consequently, to our best knowledge, it can achieve the highest visual quality of marked image for a given payload as compared with the prior arts of image reversible data hiding. The experimental results have been presented to confirm the claimed superior performance.Keywords: optimum histogram pair, reversible (lossless) data embedding, integer wavelets, selection of best threshold, adaptive histogram modification, selection of suitable embedding region.

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Abstract. This proposed scheme reversibly embeds data into image predictionerrors by using histogram-pair method with the following four thresholds for optimal performance: embedding threshold, fluctuation threshold, left-and right-histogram shrinking thresholds. The embedding threshold is used to select only those prediction-errors, whose magnitude does not exceed this threshold, for possible reversible data hiding. The fluctuation threshold is used to select only those prediction-errors, whose associated neighbor fluctuation does not exceed this threshold, for possible reversible data hiding. The left-and righthistogram shrinking thresholds are used to possibly shrink histogram from the left and right, respectively, by a certain amount for reversible data hiding. Only when all of four thresholds are satisfied the reversible data hiding is carried out. Different from our previous work, the image gray level histogram shrinking towards the center is not only for avoiding underflow and/or overflow but also for optimum performance. The required bookkeeping data are embedded together with pure payload for original image recovery. The experimental results on four popularly utilized test images (Lena, Barbara, Baboon, Airplane) and one of the JPEG2000 test image (Woman, whose histogram does not have zero points in the whole range of gray levels, and has peaks at its both ends) have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image verses original image at given pure payloads.

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This paper presents a new steganalysis scheme to attack JPEG steganography. The 360 dimensional feature vectors sensitive to data embedding process are derived from multidirectional Markov models in the JPEG coefficients domain. The class-wise non-principal components analysis (CNPCA) is proposed to classify steganograpghy in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking modern JPEG steganographic schemes -F5, Outguess, MB 1 and MB2.