Designing an efficient image Encryption-Then - Compression system
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Transcript of Designing an efficient image Encryption-Then - Compression system
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Designing an Efficient ImageEncryption-Then-Compression System via Prediction
Error Clustering and Random Permutation
Presentation by Dig Vijay Kumar Yarlagadda
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Contents• Objective• Introduction• Description• Status• Expected results• Conclusion• References
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Objective• The objective of this project is on the practical design of a pair of image
encryption and compression schemes, in such a way that compressing the
encrypted images is almost equally efficient as compressing their original,
unencrypted counterparts. Meanwhile, reasonably high level of security needs
to be ensured.
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Introduction• Consider an application scenario where a content owner wants to securely and
efficiently transmit an image to a recipient via an untrusted channel provider.
• Traditional Compression-then-Encryption (CTE) systems requires content owner
to perform both encryption and compression operations.
• Content owner wants to secure the image data through encryption with no
incentive to compress image, while channel provider is interested in compressing
the network traffic to maximise network utilization.
• It is desirable that this compression task is done by channel provider.
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Encryption-then-Compression(ETC) system
Design Problem:
• How to compress an image if it’s
data is hidden?
The solution:
• Perform all operations in prediction
error domain
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Prediction Error domain• Prediction errors are calculated in order to predict the desired target value
for new data.
• Method of calculating prediction error:
• Make a prediction i,j by using an image predictor GAP
• Calculate prediction error using formulaei,j = Ii,j - i,j
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Image Encryption
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Encryption Algorithm1. Compute the prediction errors i,j of whole image I.
2. Divide all prediction errors into L clusters Ck , for 0 ≤ k ≤ L-1
3. Reshape the prediction errors in each Ck into a 2-D block having four columns
and [|Ck|]/4] rows, where |Ck| denotes the number of prediction errors
in Ck.
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Encryption Algorithm (cont.)4. Perform two key-driven cyclical shift operations to each resulting prediction
error block, and read out the data in raster-scan order to obtain the
permuted cluster k .
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Encryption Algorithm (cont.)
5. The assembler concatenates all the permuted clusters k , for 0 ≤ k ≤ L-1, and
generates the final encrypted image.Ie = 0 1 …………….L-1
6. Pass Ie to Charlie together with the length of each cluster |k|, for 0 ≤ k ≤ L-2
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Lossless compression of Encrypted image via Adaptive Arithmetic Coding
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Lossless compression (cont.)
De-Assembler:
A De-assembler can be utilized to parse Ie into L segments 0 1 …………….L-1 in the
exact same way as that done at the encryption stage.
Adaptive Arithmetic Coding:
An adaptive AC is employed to losslessly encode each prediction error sequence k
into a binary bit stream Bk .
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Lossless compression (cont.)
Assembler:
Assembler concatenates all Bk to produce the final compressed and encrypted
bit stream B, namely,
B = B0 B1............. BL-1
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Sequential Decryption and Decompression
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Sequential Decryption and Decompression
• Using the side information |Bk|, recipient divides B into L segments Bk , each
of which is associated a cluster of prediction errors.
• For each Bk , an adaptive arithmetic decoding can be applied to obtain the
corresponding permuted prediction error sequence k .
• As recipient knows the secret key K, the corresponding de-permutation
operation can be employed to get back the original Ck .
• With all the Ck , the decoding of the pixel values can be performed in a raster-
scan order.
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Sequential Decryption and Decompression (cont.)
• For each location (i, j), the associated error energy estimator i,j and the
predicted value i,j can be calculated from the causal surroundings that have
already been decoded.
• The corresponding cluster index k can be determined using formula
k = {k|qk ≤ ∆i,j < qk+1}
• The reconstructed pixel can then be computed by
i,j = i,j + ei,j
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Security and Performance analysisTest images
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Security Analysis
Number of distinctive ways of permutation:
N =
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Compression performance• Image predictor GAP has strong de-correlation capability.
• Arithmetic coding is 2 to 3 times more effective than traditional compression
techniques.
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Status• An Encryption scheme is realized via prediction error clustering and random
permutation.
• Highly efficient compression has been realized by a context-adaptive
arithmetic coding approach.
• Security and Performance analysis yet to be performed.
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Expected resultsLossless Compression Performance:Reconstruction results of directly
decoding the compressed and encrypted images:
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Conclusion• An efficient image Encryption-then-Compression (ETC) System has been
designed. Encryption is realized via prediction error clustering and random
permutation. Highly efficient compression has been realized by a context-
adaptive arithmetic coding approach.
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References[1]. Jiantao Zhou, Xianming Liu, Oscar C. Au, and Yuan Yan Tang, "Designing an Efficient
Image Encryption-then-Compression System via Prediction Error Clustering and Random
Permutation", IEEE Trans. Inf. Forensics Security, Vol.9, No.1, Jan. 2014.
[2]. X. Wu and N. Memon, "Context-based, adaptive, lossless image codec," IEEE Trans.
Commun., vol.45, no. 4, pp. 437–444, Apr. 1997.
[3]. Rafael C.Gonzalez and Richard E. Woods, "Digital Image Processing", Second Edition,
Jan.2004.
[4]. Xiaolin Wu and Nasir Memon, "CALIC – A Context based adaptive lossless image
CODEC", IEEE Trans. Inf. Forensics Security, vol. 3, no. 4. pp. 437-444, Apr. 1997.
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THANK YOU
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Gradient Adjusted Predictor(GAP)In GAP the gradient of the intensity function at the current pixel I is estimated by computed the following quantities:
dh = |I[i-1, j] – I[i-2, j]| + |I[i, j-1] – I[i-1, j-1]| + |I[i+1, j-1] – I[i, j-1]|dv = |I[i-1, j] – I[i-1, j-1]| + |I[i, j-1] – I[i, j-2]| + |I[i+1, j-1] – I[i+1, j-2]|
A prediction is then made by the following procedure:IF (dv – dh > 80) {sharp horizontal edge}
ELSE IF (dv – dh < -80) {sharp vertical edge}
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Gradient Adjusted Predictor(GAP) (cont.)
ELSE { (w+n)/2 +(ne-nw)/4;IF (dv – dh > 32) {horizontal edge}
(w+n)/2 + (ne-nw)/4;ELSE IF (dv – dh > 8) { weak horizontal edge}
(3 + w)/4ELSE IF (dv – dh < -32) {vertical edge}
+ n)/2ELSE IF (dv – dh < -8) {weak vertical edge}
+ n)/4}
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Gradient Adjusted Predictor(GAP) (cont.)
where n = I[i, j-1]w = I[i-1, j]ne = I[i+1, j-1]nw = I[i-1, j-1]nn = I[i, j-2]ww = I[i-2, j]
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