Steganalysis versus Splicing detection Paper by: Yun Q. Shi, Chunhua Chen, Guorong Xuan and Wei Su By: Nehal Patel Siddharth Samdani. ECE643 DIGITAL IMAGE PROCESSING. Agenda.
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Steganalysis versus Splicing detection
Yun Q. Shi, Chunhua Chen, Guorong Xuan and Wei Su
From the Greek word steganos meaning “covered”– and the Greek word graphie meaning “writing”
The spliced image is a composite picture generated by combining image fragments from the same or different images without further post-processing such as smoothing of boundaries among different fragments.
B,C : Original Images A : Spliced Image
Subjective measurement for steganalysis:
Objective measurement for Steganalysis:
5/6 of authentic and 5/6 of the spliced images are used to train a SVM classifier and remaining 1/6 of these images are used to test the trained classifier.
Some Universal Steganalysis methods:
Multi-size Block Discrete Cosine Transform (MBDCT)
The image is divided into nxn non overlapping blocks. Then DCT is applied independently on each block, which gives a 2-D array consisting of BDCT coefficients of all the blocks. Using individual block size corresponding BDCT 2-D array is obtained. Each of this BDCT 2-D array generates corresponding features.
Moment Based Features
Markov based features:
The implementation results of novel natural image model on image dataset :
The ROC curves of applying the natural image model