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Benchmarking steganographic and steganalysis techniques
Electronic Imaging of SPIE 2005Authors:Kharrazi, Mehdi, Husrev T. Sencar, and Nasir
MemonDepartment of Electrical and Computer Engineering
Polytechnic University, Brooklyn, NY 11201, USA
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Outline
• Introduction• Universal steganalysis• Embedding techniques• Discussion • References
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Introduction
• specific steganalysis techniques– good results.– might fail on all other steganographic
algorithms. • Universal steganalysis techniques– less accurately.– acceptable results on new and unseen embedding
algorithms.
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Introduction
• Receiver operating characteristics (ROC)– organizing classifiers– Visualizing– true positive (TP) hit– true negative (TN) correct rejection– false positive (FP) false alarm– false negative (FN)miss
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Universal steganalysis
Find and calculate features– BSM(binary similarity measure)– Wavelet Based– Feature Based
Strong classification algorithm
Preprocessing Framing
Feature extraction methods
Classification
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Embedding techniques
• Outguess• Chooses redundant DCT coefficients which have
minimal effect on the cover image and then embed the message.• Adjust remaining coefficients in order preserve the
original histogram of DCT coefficients.
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Embedding techniques
• F5• Implementation of matrix encoding, decreases the
necessary number of changes.
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Embedding techniques
• Model based• Transformed image coefficients into two parts, replaces
the perceptually insignificant component with the coded message signal
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Embedding techniques
• PQ(Perturbed Quantization Steganography )• Re-compressed with a lower quality factor.• Modified coefficients during compression.
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Discussion
• Actual embedding rate will be lower do to the embedding overhead incurred when splitting the image into smaller blocks.
• FBS has the best performance among the 3 techniques studied.
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References
• An introduction to ROC analysis• BSM(binary similarity measure)• Wavelet Based• Feature Based• Outguess• F5• Model based• PQ
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