Benchmarking steganographic and steganalysis techniques Electronic Imaging of SPIE 2005 Authors:Kharrazi, Mehdi, Husrev T. Sencar, and Nasir Memon Department of Electrical and Computer Engineering Polytechnic University, Brooklyn, NY 11201, USA 1

Outline Introduction Universal steganalysis Embedding techniques Discussion References 2

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. 3

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 4

Universal steganalysis Find and calculate features – BSM(binary similarity measure) – Wavelet Based – Feature Based Strong classification algorithm Preprocessing Framing Feature extraction methods Classification 5

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. 6

Embedding techniques F5 Implementation of matrix encoding, decreases the necessary number of changes. 7

Embedding techniques Model based Transformed image coefficients into two parts, replaces the perceptually insignificant component with the coded message signal 8

Embedding techniques PQ(Perturbed Quantization Steganography ) Re-compressed with a lower quality factor. Modified coefficients during compression. 9

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. 10

References An introduction to ROC analysis BSM(binary similarity measure) Wavelet Based Feature Based Outguess F5 Model based PQ 11