Crypto-Compression of Images Based on The ANNs and ... - CiteSeerX

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J. of Commun. & Comput. Eng. ISSN 2090-6234 Volume 2, Issue 3, 2012, Pages 1: 6

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Crypto-Compression of Images Based on The ANNs and The AES Algorithm Y. Benlcouiri · M. Benabdellah · M. C. Ismaili · A. Azizi

Received: 10 July 2011/ Accepted: 23 July 2011

Abstract The compression and the data encryption are two technologies whose importance is growing exponentially in a myriad of applications. In addition, the excessive use of computer networks for data transfer must obviously obey to a double objective: the reduction of the volume of data in order to clutter the maximum possible public networks of communication and the confidentiality in order to ensure an optimum level of security. In this sense, and in order to ensure the optimization and securing of the transmission and storage of still images, we propose in this work a new hybrid approach for crypto-compression which applies an encryption based on the AES algorithm on the parameters of the compression by network of neuron multi layer. Keywords Compression · artificial neural networks (ANNs) · AES · Crypto-compression

1 Introduction The compression of images has become an essential task to copy with the increasing amount of information that one wishes to transmit or store. It provides techniques for reducing the number of bits in the transmission level. This allows you to increase the volume of the data transferred in a minimum time, with the reduction of the necessary cost. The latter has become increasingly important in the most computer networks. Y. Benlcouiri · M. C. Ismaili · A. Azizi Laboratory of arithmetic, Scientific Computing and Applications, Faculty of Sciences, Oujda, Morocco. M. Benabdellah Faculty of Legal, Economics and Social Sciences, Oujda, Morocco. E-mail: med [email protected]

Since the volume of data traffic began to exceed the capacity of transmission, we are obliged to discuss and use the latest techniques used for the compression of data [4]. Artificial neural networks (ANNs) were applied to the compression of images and have demonstrated their superiority in relation to traditional methods especially when it comes too noisy or incomplete. Their application to the compression of image seems to be well adapted to the particular function, because they have the possibility of pre-treatment of the input template to produce simpler models with fewer components. This compressed information (stored in a hidden layer) retains almost all the information obtained from the external environment [6]. On the other hand, the encryption or the encryption of the data is typically described from secret communication of information between two interlocutors. In a computer system, this confidentiality occurs in several forms, especially in the protection of storage, access and transmission of the information. Encryption is based on a transformation of the code representing the information into a form non-standard, so as to limit the use. The technique is very old, but it has been widely developed and transformed with the intensive use of the computer. This area remains, for obvious reasons, very secret and the most recent advances are not disclosed. This presentation is limited obviously in the techniques and tools of the public domain, which are those actually used in the large computer applications [7]. For privacy reasons, during the transfer, these data must be rendered illegible non-decipherable, therefore encrypted or coded by the algorithms by using the key for identical or dif-

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ferent encryption and decryption [8]. The approaches of crypto-conventional compression are all tend to achieve the techniques of encryption and compression in a disjunct; this posed a problem at the time of the steps in decryption and decompression, especially for the case of some areas of applications of the real time type as the emission of satellite images or even telemedicine or the time factor is critical. Thus new mixed approaches for crypto-compression are beginning to grow. The concept was to combine both techniques of encryption and compression in a way that they are carried out in a manner attached [4]. The challenge is always to provide for all of our applications a volume of data in smaller size and privacy robust. It is then to arrive to find an effective approach for crypto-compression. It is in this theme that our work is located; we are trying to describe the process of a hybrid technology for crypto-compression based on the method of ANNs and AES. In what follows, we will refer to the compression of images by networks of neurons, and then we begin the encryption of images by the AES algorithm. In part 3, we will discuss our method of crypto-compression and the results obtained after its application. Conclusions as well as a few prospects are data in the last section.

2 Methods 2.1 Compression by ANNs Networks of neurons are originally an attempt for mathematical modelling of the human brain. The main idea of these networks is that it is a single unit, a neuron, which is capable of achieving some basic calculations. Then we connect them by a significant number of these units and we tried to determine the calculation power of the network thus obtained. It is important to note that these neurons manipulate digital data and not symbolic [2]. At the compression level of images, there was already a comprehensive number of the application of the ANNs and many learning algorithms and different architectures have been used. The networks of neurons to architecture of multilayer perceptron (MLP) were experienced to allow compression of image data, they are divided into two parts: network compressor which constitutes the input layer and the hidden layer, and network decompressor which represents the result of the neural network (See Figure 1). The rate of compression is designated by the ratio between the number of neurons in the hidden layer and that of the input layer [3].

Fig. 1: Diagram of the network of neuron for the compression and decompression. For a ANNs, the learning may be regarded as the problem of updating the weights of connections within the network, in order to succeed the task that is requested for it. Learning is the main characteristic of ANNs and it can be done in different ways, and according to different rules. The learning objective is to adjust the weight of connection in such a way that the data presented to the input layer would have roughly the same as those resulting in the output layer [1]. Using the following the algorithms of retropropagation of the gradient with the sigmoid activation function: f (x) = 1/(1 + e−x ) f ′ (x) = f (x) ∗ (1 − f (x)) On images of learning subdivided into block of fixed size then come as follows: > Repeat for each block 1 Affect the Xi (the data of the block) to the cells in the input layer. 2 Until the quadratic error is greater than the desired threshold on the outputs obtained in relation to the entry, Calculate the hi (the states of the neurons in the hidden layer) ∑ hj = Xi × Wij i

aj = f (hj )

Crypto-Compression of Images Based on The ANNs and The AES Algorithm

Then the X¯i (the states of neurons in the output layer) ∑ ′ X¯i = aj × Wji j

ai = f (X¯i ). Calculate the error on the units of the cells in the output layer δi = erri × f ′ (X¯i ) and then those of the hidden layer: ( ) ∑ ′ δj = Wji × δi × f ′ (hj ). i

Updated the weight on the output units and those of the hidden units ′ ∆Wji

= ε × ∂i × aj

∆Wji = ε × ∂i × Xi End Until > End Repeat. 2.2 AES Encryption AES is the acronym of Advanced Encryption Standard, creates by Johan Daemen and Vincent Rijmen. It is a technique of encoding to symmetrical key. It is the result of a call to world contribution for the definition of an algorithm of encoding, call resulting from the national institute of the standards and technology of the government American (NIST) in 1997 and finished in 2001. this algorithm provides a strong encoding and was selected by the NIST like normalizes federal for the data processing (Federal Information Processing Standard) in November 2001 (FISP-197), then in June 2003, the American government (NSA) announced that AES was sufficiently protected to protect the information classified up to the level TOP SECRET, which is the most level of safety defined for information which could cause ”exceptionally serious damage” in the event of revelations with the public. Algorithm AES uses one the three lengths of key of coding (password) following: 128, 192 or 256. Each size of key of encoding uses a slightly different algorithm, thus the higher sizes of key offer not only one greater number of bits of jamming of the data but also an increased complexity of the algorithm [4]. This algorithm always preserves the high level of safety proposed by DES; indeed the process is al-ways based

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on a function of expansion E, boxes of substitutions S, called on the level of the diversification of key K; moreover, the process always preserves the principle of the stages at the moment of the stage of expansion. The innovation brought by the AES is noted on the level of the size of the secret key as well as the size of the data treated in entry; precisely we pass from a key of coding of size 64 bits (8 bytes) for the case of worms a key of size doubles 128 bits (16 bytes) for the AES [7]. The size of the data with crypter is as notably larger as that of since we pass from 64 bits towards 128 bits. Moreover, as for DES, the AES is a cryptographic system with secret key; what makes the operation of Encryptingdecrypting rather light. The size of the data treated by the AES (16 bytes) gives us the possibility well of exploiting the supporting algorithm in applications of the data files of large size [5]. Cryptography with symmetrical algorithms uses the same key for the processes of Encrypting and Decrypting; this key is generally called ”secret” (in opposition to ”private”) because all the safety of the unit is directly related to the fact that this key is known only by the shipper and the recipient [6]. Symmetrical cryptography is very much used and is characterized by a great speed (encrypting with the flight, ”one-the-fly), implementations as well software (Krypto Zone, Firewalls software Firewall-1 type and VPN-1 of Checkpoint) that hardware (dedicated charts, processors crypts 8 to 32 bits, c) what accelerates the flows clearly and authorizes its massive use. This type of cryptography usually functions according to two different processes, encrypting per blocks and the encrypting of ”stream” (uninterrupted). Algorithm AES is iterative (See Figure 2). It can be cut out in 3 blocks: Initial Round: It is the first and the simplest of the stages. It counts only one operation: Add Key Round. N Rounds: N is the iteration count. This number varies according to the size of the key used. 128 bits for N=9, 192 bits for N=11, 256 bits for N=13. This second stage consists of N iterations comprising each one the four following operations: Sub Bytes, Rows Shift, Mix Columns, Add Key Round. Final Round: This stage is almost identical to the one of the N iterations of the second stage. The only difference is that it does not comprise the operation Mix Columns.

3 Proposed Method The introduced approach is based on two algorithms: one to compress and encrypt the image and another

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Fig. 3: Diagram of principle Crypto-compression algorithm.

Fig. 2: Diagram block of the algorithm AES, version 128 bits. to reconstruct the image. These two algorithms are explained as follows: 3.1 Algorithm of Crypto-Compression We divide the image into blocks. Each block has size (m ∗ m′ ) equal to n representing the number of layers of input ANNs adopted. Then we are putting in the compressor of the neural network to obtain the data compressed on the hidden layer of the network used hi. Then, these results are encrypted using the AES algorithm. Finally, our crypto-compressed image can be transmitted safely, (See Figure 3). 3.2 Reverse Algorithm of Crypto-Compression To rebuild the image, the received data must be decrypted by the reverse algorithm AES, which makes us come back already to the hi which is previously encrypted. The decompression of these values continues on the route from the party decompressor of ANNs which is responsible to return the blocks of the image, as already shown in the following figure, (See Figure 4): 4 Applications The application of the method for crypto-compression proposed ANNs-AES, using ANNs whose architecture

Fig. 4: Diagram of principle reverse crypto-compression algorithm.

is 64 neurons on the layer of entries, 13 neurons in the hidden layer and 64 neurons in the output layer with the AES-128 bit on five biomedical images, gives us the following results (Figure 5). It should be noted that the resolution of the images is 256 × 256dpi, and the processor used is Intel Pentium4 for a rate equalizes 3.2 GHz. The results obtained are given on the Table 1. The compression by networks of neurons gives a rate of very interesting compression and a superior quality even if it is costly in terms of time for the adjustment of adequate weight to the networks of neuron which are responsible for the visual quality of the processed image, a task which could be assigned to the server view to the computing power available to it. Decompression, which will be performed by the client at a distance, is more rapid. In addition, our method provides a framework for transmission with interlacing. Transmission (progressive) which decreases the workload of the bandwidth. As regards the time necessary for the operations of encryption and

Crypto-Compression of Images Based on The ANNs and The AES Algorithm

Original image 1

Reconstructed image 1

Original image 2


Original image 3

Original image 4



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Table 1: Result of our encryption-compression method ANNs-AES applied in the five medical images (1, 2, 3, 4 and 5). T. Decom.: Time of Decompression. PSNR: Peak Signal to Noise Ratio. E.O.I.: Entropy of Original Image. E.R.I.: Entropy of Reconstructed Image. T. App.: Time for learning the network of neurons. S.O.I.: Size of original image. S.R.I.: Size of reconstructed image. Our crypto-compression method has shown to be effective on the compression ratio, thus provides as well as the visual quality of reconstructed images. The principal advantages of our approach are the flexibility and the reduction of the processing time, which is proportional to the number of the dominant coefficients used after compression by ANNs, at the time of the operations of encryption and decryption. Indeed, by our method, one can vary the processing time according to the desired degree of safety.

Original image 5


Fig. 5: Results obtained after the application of our method on the images (1), (2), (3), (4), (5).

decryption can be varied depending on the level of security.

5 Conclusion We are interested in a compression technique using the ANNs to develop a new technical hypride for cryptocompression, checking a number of constraints and with a good robustness. We have used and tested a wide array of images, and each time an evaluation test was

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conducted to obtain a more objective view about robustness of the proposed approach. These tests have shown that the technique is efficient. It would be beneficial and necessary to further deepen the study of this technique and for the further comparison with other existing methods. As the application is oriented toward the crypto-compression of fixed images, an extension of the method in the field of video (moving images) may be considered. We can also combine this technique with other techniques for crypto-compression in order to further improve the robustness. We expect, in our future work, the use of methods for the compression in order to decrease the time of learning as the networks of wavelets and in parallel we will look at the cryptanalysis of the proposed method.

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