To manage the data the encrypted dummy data, we employ the inverse wavelet transform WT for reconstructing the two parts real and imaginary into single unit dummy data. The inverse wavelet with the Haar transform is employed. The subband data C 0 is the low-passed and the diagonal detail zeros valued data.
The above reconstruction process with the inverse wavelet transform IWT gives an information dummy data E after uniting the real and imaginary parts of D in reconstruction phase. We use the inverse of S-box to decrypt the ciphered image. Fig 3 A , 3 B and 3 C show the true, encrypted and the decrypted image of Signature. One can see that our algorithm is capable to recover the true secret image with high accuracy as measured out in terms of coefficient correlation. A original image, B encrypted image, and C decrypted image, respectively. A true image, B S-box encrypted image, C proposed method encrypted image, D decrypted image using inverse S-box, E decrypted image using inverse proposed method, F — G histogram gradient estimation of information data listed in first row from A - E.
The opposite of an encryption process is decryption process.
The wavelet transform WT is considered to decompose the encrypted data image E into four subband data sets , , , and. The high frequency subband data and are carrier communication data sets that are preserved in the same position. The retrieved imaginary and real parts of the scrambled data are rejoined in complex data pattern.
Lastly the obtained complex scrambled data is refined by the inverse Fresnelet transforms applying the exact keys specification of the Fresnelet transform for the sake of obtaining the private information image on employing the inverse cipher S-box. In this section we gauge the security strength of the proposed algorithm by using some most significant statistical analysis, s described below.
These parameters are employed in the Fresnelet transform operations for the encryption as well as decryption stages and are considered as the key parameters. The obtained information images are predicted through the evaluation of correlation coefficients CC with the original information image data. The index of CC followed with in the range of 0 to 1.
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- Robustness Analysis of a Hyperchaos-Based Digital Image Encryption Algorithm under Noise Attacks.
- 1 Introduction?
To analyze the presented algorithm, our main focus on encryption analysis. The decrypted out put is almost clear with average value in terms of coefficient correlation value 0. Notice from Table 3 that the given scheme provides good encryption of secret information data as compared to ordinary S-box encryption methods [ 2 ]. It is an important feature of an efficient encryption algorithm to elapse lesser processing time.
Keeping this fact in view, classically, there are four main attacks [ 36 ], that are worth-studying. To overcome the drawback of general S-box encryption in highly contrast data, this paper presents a new encryption approach with improved cipher security of the encrypted data. The proposed method is considered better approach for two significant reasons.
First, the Fresnelet transform is used for encrypting the data of information image with various distance parameters as key. Second, the algebraic s-box provide highly nonlinearity in an encrypted data. Experimental studies validate that an encryption of digital image using the proposed scheme shown great superiority over the recently existing encryption methods. Although the proposed method in this study intents at the image encryption, however, its limitation does not restrict it to only this area and can be significantly implied in various fields of security information.
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Rest of the data used in the proposed method are available within the paper.
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Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract In this paper, a unique and more distinctive encryption algorithm is proposed. Data Availability: All relevant data are within the paper. Funding: The authors received no specific funding for this work.
Introduction Data encryption is an integral part of multimedia technologies.
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Fresnelet transform The propagation phenomena of wave are structured via diffraction principle using the Fresnel transform [ 43 ]. Following this, the four combinations are obtained using the tensor product for producing the approximation lower-lower subband and three high frequency details lower-high subband, high-lower subband, and high-high subband as follow: 10 11 12 13 In 10 — 13 , the representation of scaling functions is done using x and representation of the wavelet functions is done using y.
Download: PPT. Fig 1. Algorithm for algebraic S-box In this section, we describe the algorithm used for construction of our S-box.
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Performance analysis of S-box In this section, we analyze the proposed S-box through some widely accepted parameters. Nonlinearity The nonlinearity is a measure of the minimum distance of the reference function from the set of all the affine functions [ 2 ]. Table 2. Strict avalanche criterion This criterion is used to gauge the confusion creating capability of an S-box.
Bit independence criterion In bit independence criterion, input bits are altered exclusively, and then output bits are scrutinized for their independence. Encryption method The secret image is encrypted to keep the confidential information, the Fresnelet transform is employed based on the wavelet family, known as Haar. Single unit dummy data To manage the data the encrypted dummy data, we employ the inverse wavelet transform WT for reconstructing the two parts real and imaginary into single unit dummy data. Fig 3.
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Fig 5. The decryption process The opposite of an encryption process is decryption process. Statistical analysis In this section we gauge the security strength of the proposed algorithm by using some most significant statistical analysis, s described below. Entropy : Entropy analysis measures the randomness of a system.
Table 3 , represents the information entropy for different images both original and the encrypted. It is obvious that the results favor the proposed methodology. Contrast : The contrast analysis is used to identify objects in an image. The numerical results for contrast of t images true and encrypted images are arranged in Table 3 , which show that the proposed scheme is efficient. Correlation : The correlation coefficient, between the pixels at the same indices in both the plain and the encrypted image, measures the similarity between the pixels pattern of both images. Results arranged in Table 3 witness effectiveness of the proposed method.
Differential analysis : A desirable feature of an encryption algorithm is to show high sensitivity to single-bit change in the plain image. NPCR stands for the number of pixels change rate of image as a result of one pixel change in the plain image. However UACI means unified average intensity of differences between the plain and the encrypted images.
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These results prove that our algorithm is highly sensitive and robust against the differential attacks and even single-bit difference in two plain image results in absolutely different encrypted images. Processing speed It is an important feature of an efficient encryption algorithm to elapse lesser processing time. Ciphertext only attack : When a string of ciphertext is known to the attacker.
Known Plaintext attack : When a string of plaintext along with the corresponding ciphertext is known to the attacker. Chosen plaintext attack : When attacker has temporary access to the encryption mechanism such that he can choose a plaintext string and obtain its ciphered version. Chosen ciphertext attack : When attacker has temporary access to the decryption mechanism such that he can choose a ciphertext string and can obtain its corresponding plaintext string.