As mentioned in section I, the systems are designed to be able to encrypt messages before steganography operations. The question here is, first of all, what do we need to do the encryption operation before hiding? Second, is the security of steganography algorithms increased by considering cryptographic operations? Third, did not change the entropy of the embedded message using encryption algorithms and the file containing the message will not be suspicious?
In this section, by mentioning two algorithms in image and two algorithms in audio, and its important points, the above questions will be answered. Also, with the evaluations that are done on them, their advantages and disadvantages will be clearly understood.
Steganography must not be confused with cryptography that involves the conversion of the message, so that its meaning is unclear to the malicious people who track it. In this context, the definition of failure of a steganographic system is different from that of a cryptographic system. In cryptography, when an attacker accesses the encryption key and can read the confidential message, the cryptographic system crashes and the algorithm fails. Steganography algorithm failure occurs when an attacker only detects that the stenographic system has been used and that she is able to read the embedded message. According to [1], steganography provides a tool for covert communication between the sender and receiver, which cannot be ignored without a significant change in the data embedded in it. In addition, in classical steganographic systems integrated with cryptographic algorithms, the security of their system depends on the confidentiality of its encrypted algorithm. Therefore, when the encryption system is revealed, the steganography system will fail [4].
Therefore, it can be said that in order to increase the security of the multi-layer, the use of cryptographic algorithms in steganographic systems is always a good idea. But this will lead to challenges that will be addressed continuation. By combining them, the data can be encrypted by an encryption algorithm and then the ciphertext can be embedded in the image, audio or any other medium with or none the help of the stage key. As mentioned, the combination of two methods of cryptography and steganography will increase the security of embedded data. But this type of combination will meet needs such as the capacity of the medium, the security of the embedded data, and the robustness of the combined algorithm for transmission of the secure data over an open channel [7].
Since the purpose of this paper is not to explain steganography and steganalysis algorithms in details, a general explanation is provided for them. In the following, by presenting two steganography algorithms for image and two steganography algorithms for audio, the cryptographic combination with each of these two algorithms is investigated.
The First, algorithm A is explained, so that this algorithm is designed and implemented for both image and audio media. This algorithm is implemented for image and audio on JPEG and WAV format, respectively. This algorithm is implemented using a symmetric encryption operation and without using it with the maximum capacity of the message that can be inserted. In the following, the algorithm B will be explained, so that this algorithm is also designed and implemented on the image media on JPEG format. Also, in section C, an audio algorithm on WAV format is designed and implemented. Similar to the algorithm described in section A, each of the algorithms designed in sections B and C, have been implemented using a symmetric encryption algorithm and without using it with the maximum capacity of the message that can be inserted. Therefore, it is observed that all conditions except the steganography algorithm are considered the same.
As mentioned, one of the most important evaluation criteria in steganography algorithms is to examine ROC curves and confusion matrices, which here, in addition to reviewing them, will be examined the maximum message capacity of both algorithms with and without the use of cryptographic algorithms, and the results will be obtained. Both steganography algorithms are evaluated using steganalysis algorithms and their graphs are examined as follow.
Steganalysis algorithms in image, which are the reference for the two steganography algorithms A and B, examine the features of NJ inter block [8], NJ intra block [8], spam [9], DCTR [10] and CCPEV[11, 12], for raw and stego media. In [8], a new method of JPEG steganalysis based on observation of the bivariate generalized Gaussian distribution in the Discrete Cosine Transform (DCT) domain is proposed, which extracts the features of the neighboring density inter-block and intra-blocks. In [8], a method for detecting steganography methods is presented in which the insertion operations are performed in the spatial domain by adding a low-amplitude domain independent of the stego signal. In [10], introduced a set of new features for steganalysis of JPEG images. These features are engineered as first-order statistics of quantized noise residues from a decompressed JPEG images using 64 kernels Discrete Cosine Transform (so-called underestimated DCTs). In [11, 12], for the steganalysis JPEG images, the features that are extracted directly from the embedded domain from the DCT coefficients have the best performance. The purpose of this paper is to build a new multi-class JPEG steganalyzer with completely improved performance. They first expand 23 sets of DCT features and then do so by applying calibration to Markov features and reducing their dimensions. The resulting feature set is merged and a 274-dimensional feature vector appears.
Also, steganalysis algorithms in audio, which are the reference for the two steganography algorithms A and C, examine the various features. In [13], were examined four distance criteria to audio steganalysis, namely perceptual audio quality measure (PAQM), spectral phase distortion (SPD), and log-likelihood ratio (LLR and log-area ratio (LAR). In [14], considered features such as Markov transition and neighboring joint density of the MDCT coefficients. In this paper, IM and INJ marked inter-frame Markov and inter-frame neighboring joint density features. In addition to the mentioned feature set, second-order derivative-based audio steganalysis is done. In [15], the inter-frame pattern is has changed across adjacent frames. Based on this analysis, they attempted to design feature sets for evaluating frames using second-order derivative based spectrum analysis. The features mentioned. In this paper are: (1) Statistical model and signal complexity, that demonstrate the distribution of the violence amount of pixels: Markov random field models (MRFs); Gaussian mixture models (GMMs); and generalized Gaussian density models (GGD) in transform domains. (2) Moment statistics of GGD shape parameter on inter-frame, which is based on spectral distribution analysis, they hypothesized that the hidden behavior mutates the persistence of the distribution of adjacent frames. (3) Frequency-based sub band moment statistics, where second-order derivatives are widespread used for detecting celibate points, margins and so on. The method of extracting signals is based on second-order derivatives statistics: second-order derivatives from 576 MDCT sub band signals through all frames. Calculate statistics, containing mean value, standard deviation, skewness, and kurtosis of sub band signals. Eventually (4) accumulative neighboring joint density and Markov method, where they planned an inter-frame Markov approach (IM) and inter-frame Neighboring Joint Density (INJ) for MP3. In [16], presented a method that is based on taking advantages of R-MFCC coefficients, which are based Human auditory system. In this paper mentioned Pitch ratio and Mel Scale and Mel-frequency cepstral coefficients metrics. Also considered the feature set including some of above features as custom features that it evaluates for the same dataset.
All of the above-mentioned steganalysis algorithms are implemented by MATLAB® and evaluated using ensemble classifier [17].
4.1. Algorithm A (Implemented in Image and Audio)
This algorithm is presented in [18] and implemented on JPEG images and wav audios. In this paper, in order to minimize additive distortion in steganographic algorithms, a complete practical method using general (non-binary) embedding operation is proposed. This paper presents a general method for insertion while the performance of the desired additive distortion is minimized with near theoretical performance. They have a complete way to solve finite payload-limited and distortion-limited sender. The implementation described in this paper adapts them to the problem using standard signal processing tools convolutional codes with trellis quantizer. All of the results summarized at continuation.
Evaluation results without the use of cryptographic algorithm are presented as a confusing matrix of 10, 50 and 100% capacity in all media, which is shown in Fig 1. Also, ROC curves are shown in Fig 2 only with maximum payload without the use of encryption algorithm. In the following in Table 1, the testing error and AUC values are specified for the above-mentioned steganalysis algorithms.
Table 1. TESTING ERROR AND AUC WITHOUT USING THE ENCRYPTION ALGORITHM IN IMAGE-INSERTION IN ALGORITHM A [18]
Parameters
|
NJ inter block
|
NJ intra block
|
SPAM
|
DCTR
|
CCPEV
|
Testing Error
|
0.4359
|
0.4300
|
0.3925
|
0.2418
|
0.3941
|
AUC
|
0.5910
|
0.5975
|
0.6603
|
0.8425
|
0.6530
|
Table 2 shows maximum payload without using the cryptographic algorithm, which are presented the number of media and their average capacity.
Table 2. MAXIMUM PAYLOAD WITHOUT USING THE CRYPTOGRAPHIC ALGORITHM IN IMAGE-INSERTION IN ALGORITHM A [18]
The Capacity of Media (Kbit)
|
2676
|
Average capacity
|
5913
|
Evaluation results with the use of encryption algorithm AES-256 are presented as a confusing matrix of 10, 50 and 100% capacity in all media, which is shown in Fig 3. Also, ROC curves are shown in Fig 4 only with maximum payload with the use of cryptographic algorithm.
In the following in Table 3, the testing error and AUC values are specified for the same steganalysis algorithms.
Table 3. TESTING ERROR AND AUC WITH ENCRYPTION ALGORITHM AES-256 IN IMAGE-INSERTION IN ALGORITHM A [18]
Parameters
|
NJ inter block
|
NJ intra block
|
SPAM
|
DCTR
|
CCPEV
|
Testing Error
|
0.4355
|
0.4312
|
0.3934
|
0.2459
|
0.3914
|
AUC
|
0.5916
|
0.5937
|
0.6595
|
0.8394
|
0.6507
|
Table 4 shows maximum payload with encryption algorithm AES-256, that are presented the number of media and their average capacity.
Table 4. MAXIMUM PAYLOAD WITH ENCRYPTION ALGORITHM AES-256 IN IMAGE-INSERTION IN ALGORITHM A [18]
The Capacity of Media (Kbit)
|
2676
|
Average capacity
|
5479
|
4.2. Algorithm B (Implemented in image)
This algorithm is designed and implemented quite simply, and so will be no resistance to evaluators. In this paper, the secret message in the LSB method is placed in space Ycbcr of the cover images.
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)
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)
Evaluation results in this algorithm without the use of cryptographic algorithm are presented as a confusing matrix of 10, 50 and 100% capacity in all media, which is shown in Fig 5. Also, ROC curves are shown in Fig 6 only with maximum payload without the use of encryption algorithm.
In the following in Table 5, the testing error and AUC values are specified for the above-mentioned steganalysis algorithms.
Table 5. TESTING ERROR AND AUC WITHOUT USING THE ENCRYPTION ALGORITHM IN IMAGE-INSERTION IN ALGORITHM B
Parameters
|
NJ inter block
|
NJ intra block
|
SPAM
|
DCTR
|
CCPEV
|
Testing Error
|
0.3388
|
0.3173
|
0.3109
|
0.0980
|
0.2504
|
AUC
|
0.7266
|
0.7488
|
0.7536
|
0.9692
|
0.8318
|
Table 6. MAXIMUM PAYLOAD WITHOUT USING THE CRYPTOGRAPHIC ALGORITHM IN IMAGE-INSERTION IN ALGORITHM B
The Capacity of Media (Kbit)
|
2676
|
Average capacity
|
7056
|
Table 6 shows maximum payload in this algorithm without using the cryptographic algorithm, which are presented the number of media and their average capacity.
Evaluation results in this algorithm with the use of encryption algorithm AES-256 are presented as a confusing matrix of 10, 50 and 100% capacity in all media, which is shown in Fig 7. Also, ROC curves are shown in Fig 8 only with maximum payload with the use of cryptographic algorithm.
In the following in Table 7, the testing error and AUC values are specified for the same steganalysis algorithms. Table 8 shows maximum payload in this algorithm with encryption algorithm AES-256, that are presented the number of media and their average capacity.
Table 7. TESTING ERROR AND AUC WITH ENCRYPTION ALGORITHM AES-256 IN IMAGE-INSERTION IN ALGORITHM B
Parameters
|
NJ inter block
|
NJ intra block
|
SPAM
|
DCTR
|
CCPEV
|
Testing Error
|
0.2532
|
0.2347
|
0.2456
|
0.0636
|
0.1618
|
AUC
|
0.8266
|
0.8473
|
0.8333
|
0.9854
|
0.9175
|
Table 8. MAXIMUM PAYLOAD WITH ENCRYPTION ALGORITHM AES-256 IN IMAGE-INSERTION IN ALGORITHM B
The Capacity of Media (Kbit)
|
2676
|
Average capacity
|
6347
|
By viewing the Fig 2, 4 and Table 1, 3 it can be seen that if the steganography algorithm is designed in such a way that the ROC is acceptable to most of the steganalysis algorithms, the use of the cryptographic algorithm can give it a higher security factor. But if the steganography algorithm is designed in such a way that the ROC does not have acceptable over most of the steganalysis algorithms, using a cryptographic algorithm will not only increase its security factor, but will also make it more suspicious than its images (media). This point can be clearly seen by looking at Fig 6, 8 and Table 5, 7.
Also, by viewing the Table 2, 4 in algorithm A and Table 6, 8 in algorithm B it can be seen that the capacity of the message is one of the disadvantages of using cryptographic algorithms in steganography. As can be seen in each of the above algorithms, the use of the AES encryption algorithm has made the capacity of the inserted message less than when this algorithm is not used. Therefore, it can be said that in some cryptographic algorithms that change the size of the main message after encryption operation, their use in steganography algorithms will reduce the capacity of the insertable message.
4.3. Algorithm C (Implemented in audio)
This algorithm is presented in [19] and implemented on wav audios. In this paper, a wav audio file is received and placed at the least significant bit of message as binary form.
![](data:image/png;base64,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)
The work process is as follow. Samples of each data are extracted by obtaining a sample rate of that audio and it is considered as input data by adding 0.5 units. It then inserts each of the message bits from the beginning of the audio into the least significant bit of the audio, and finally written the data and creates the stego file by adding the same 0.5 unit to data sample. As can be seen, this algorithm is designed and implemented quite simply, and so will be no resistance to evaluators and it has many weaknesses.
The dataset used in the implementation of this paper is the TIMIT dataset [20], which is considered to be the most difficult condition for steganography because it is produced in particularly acoustic methods. The TIMIT data set of etude speech has been created by the voices of several individuals' men and women to provide speech data for acoustic-phonetic investigation studies and for the assessment of automatic speech distinction systems. This data set is recorded with very high quality so that there are 630 people or speakers who speak with 8 different dialects of American English in which each person has read 10 phonetically rich sentences.
By referring to algorithm A, and this time implementing it in wav format, the following results will be obtained.
Like before, evaluation results without the use of cryptographic algorithm are presented as a confusing matrix of 10, 50 and 100% capacity in all audio media, which is shown in Fig 9. Also, ROC curves are shown in Fig 10 only with maximum payload without the use of encryption algorithm.
In the following in Table 9, the testing error and AUC values are specified for the above-mentioned steganalysis algorithms in audio media. Also, Table 10 shows maximum payload without using the cryptographic algorithm, which are shown the number of media and their average capacity for TIMIT dataset.
Table 9. TESTING ERROR AND AUC WITHOUT USING THE ENCRYPTION ALGORITHM IN AUDIO-INSERTION IN ALGORITHM A [17]
Parameters
|
Footprints
|
IMINJ
|
QMDCT
|
RMFCC
|
Custom Features
|
Testing Error
|
0.4892
|
0.4871
|
0.4813
|
0.4799
|
0.4991
|
AUC
|
0.5108
|
0.5129
|
0.5187
|
0.5201
|
0.5009
|
Table 10. MAXIMUM PAYLOAD WITHOUT USING THE CRYPTOGRAPHIC ALGORITHM IN AUDIO-INSERTION IN ALGORITHM A [18]
The Capacity of Media (Kbit)
|
6300
|
Average capacity
|
93456
|
In follow, evaluation results with the use of encryption algorithm AES-256 are presented as a confusing matrix of 10, 50 and 100% capacity in all audio media, which is shown in Fig 11. Also, ROC curves are presented in Fig 12 only with maximum payload with the use of cryptographic algorithm.
Therefore, in Table 11, the testing error and AUC values are specified for the same steganalysis algorithms in audio media. Table 12 presented maximum payload with encryption algorithm AES-256, that are shown the number of media and their average capacity for the same TIMIT dataset.
Table 11. TESTING ERROR AND AUC WITH ENCRYPTION ALGORITHM AES-256 IN AUDIO-INSERTION IN ALGORITHM A [18]
Parameters
|
Footprints
|
IMINJ
|
QMDCT
|
RMFCC
|
Custom Features
|
Testing Error
|
0.4453
|
0.4284
|
0.4175
|
0.4617
|
0.4046
|
AUC
|
0.5547
|
0.5716
|
0.5825
|
0.5383
|
0.5954
|
Table 12. MAXIMUM PAYLOAD WITH ENCRYPTION ALGORITHM AES-256 IN AUDIO-INSERTION IN ALGORITHM A [18]
The Capacity of Media (Kbit)
|
6300
|
Average capacity
|
88209
|
Now, we will evaluate algorithm C. Evaluation results in algorithm C without the use of cryptographic algorithm are presented as a confusing matrix of 10, 50 and 100% capacity in all audio media, which is presented in Fig 13. Also, ROC curves are shown in Fig 14 only with maximum payload without the use of encryption algorithm.
Figure 14. ROC curve by maximum payload without using the encryption algorithm in audio-Algorithm C
In the following in Table 13, the testing error and AUC values are specified for the above-mentioned steganalysis algorithms for audio media. Table 14 shows maximum payload in this algorithm without using the cryptographic algorithm, which are shown the number of media and their average capacity.
Table 13. TESTING ERROR AND AUC WITHOUT USING THE CRYPTOGRAPHIC ALGORITHM IN AUDIO-INSERTION IN ALGORITHM C
Parameters
|
Footprints
|
IMINJ
|
QMDCT
|
RMFCC
|
Custom Features
|
Testing Error
|
0.3983
|
0.4336
|
0.4421
|
0.4793
|
0.3689
|
AUC
|
0.6017
|
0.5664
|
0.5579
|
0.5207
|
0.6311
|
Table 14. MAXIMUM PAYLOAD WITHOUT USING THE CRYPTOGRAPHIC ALGORITHM IN AUDIO-INSERTION IN ALGORITHM C
The Capacity of Media (Kbit)
|
6300
|
Average capacity
|
12568
|
Evaluation results in this algorithm with the use of encryption algorithm AES-256 are presented as a confusing matrix of 10, 50 and 100% capacity in all audio media, which is shown in Fig 15. Also, ROC curves are shown in Fig 16 only with maximum payload with the use of cryptographic algorithm.
Then in Table 15, the testing error and AUC values are specified for the same steganalysis algorithms for audio media. Table 16 is presented maximum payload in this algorithm with encryption algorithm AES-256, that are shown the number of media and their average capacity.
Table 15. TESTING ERROR AND AUC WITH ENCRYPTION ALGORITHM AES-256 IN AUDIO-INSERTION IN ALGORITHM C
Parameters
|
Footprints
|
IMINJ
|
QMDCT
|
RMFCC
|
Custom Features
|
Testing Error
|
0.2533
|
0.2405
|
0.2393
|
0.0893
|
0.1644
|
AUC
|
0.7467
|
0.7595
|
0.7607
|
0.9107
|
0.8356
|
Table 16. MAXIMUM PAYLOAD WITH ENCRYPTION ALGORITHM AES-256 IN AUDIO-INSERTION IN ALGORITHM C
The Capacity of Media (Kbit)
|
6300
|
Average capacity
|
11876
|
By viewing the Fig 10, 12 and Table 9, 11 it can be seen that if the steganography algorithm is designed in such a way that the ROC is acceptable to most of the steganalysis algorithms, the use of the cryptographic algorithm can give it a higher security factor. But if the steganography algorithm is designed in such a way that the ROC does not have acceptable over most of the steganalysis algorithms, using a cryptographic algorithm will not only increase its security factor, but will also make it more suspicious than its images (media). This point can be clearly seen by looking at Fig 14, 16 and Table 13, 15.
Also, by viewing the Table 10, 12 in algorithm A and Table 14, 16 in algorithm C it can be seen that the capacity of the message is one of the disadvantages of using cryptographic algorithms in steganography. As can be seen in each of the above algorithms, the use of the AES encryption algorithm has made the capacity of the inserted message less than when this algorithm is not used. Therefore, it can be said that in some cryptographic algorithms that change the size of the main message after encryption operation, their use in steganography algorithms will reduce the capacity of the insertable message.