Small Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain Using SVM

Shankar, Deepa D.; Azhakath, Adresya Suresh

doi:10.1007/978-981-15-5243-4_25

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The F5 uses matrixembedding coding which decreases the number of embedding changes, especially for smaller payloads. The working of F5 is as follows 64 .…”

Section: Least Significant Bit Replacement (Lsb Replacement)mentioning

confidence: 99%

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Shankar

Azhakath²

2023

Sci Rep

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The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet Security frameworks like Information hiding and detection. Examples of domains of Information hiding and detection are Steganography and steganalysis respectively. This work focus on addressing possible security breaches using Internet security framework like Information hiding and techniques to identify the presence of a breach. The work involves the use of Blind steganalysis technique with the concept of Machine Learning incorporated into it. The work is done using the Joint Photographic Expert Group (JPEG) format because of its wide use for transmission over the Internet. Stego (embedded) images are created for evaluation by randomly embedding a text message into the image. The concept of calibration is used to retrieve an estimate of the cover (clean) image for analysis. The embedding is done with four different steganographic schemes in both spatial and transform domain namely LSB Matching and LSB Replacement, Pixel Value Differencing and F5. After the embedding of data with random percentages, the first order, the second order, the extended Discrete Cosine Transform (DCT) and Markov features are extracted for steganalysis.The above features are a combination of interblock and intra block dependencies. They had been considered in this paper to eliminate the drawback of each one of them, if considered separately. Dimensionality reduction is applied to the features using Principal Component Analysis (PCA). Block based technique had been used in the images for better accuracy of results. The technique of machine learning is added by using classifiers to differentiate the stego image from a cover image. A comparative study had been during with the classifier names Support Vector Machine and its evolutionary counterpart using Particle Swarm Optimization. The idea of cross validation had also been used in this work for better accuracy of results. Further parameters used in the process are the four different types of sampling namely linear, shuffled, stratified and automatic and the six different kernels used in classification specifically dot, multi-quadratic, epanechnikov, radial and ANOVA to identify what combination would yield a better result.

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“…The F5 uses matrixembedding coding which decreases the number of embedding changes, especially for smaller payloads. The working of F5 is as follows 64 .…”

Section: Least Significant Bit Replacement (Lsb Replacement)mentioning

confidence: 99%

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Shankar

Azhakath²

2023

Sci Rep

Self Cite

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“…Then, the optimal features from each cluster were chosen for a final decision aimed at improving the overall performance of the steganalysis. Shankar and Azhakath [108] explored four feature extractions for the steganalysis, which were first order, extended DCT, second order, and Markov features. They used the LSBM method [73] and F5 [70] for the spatial domain and transform domain, respectively.…”

Section: ) Transform Domain Steganalysismentioning

confidence: 99%

“…Transform and Spatial J-UNIWARD [29], UED [37] , nsf5 [66], S-UNIWARD [29], and MiPOD [32] BOSSbase 1.01 Shankar et al [108] The four extracted features are: first order, extended DCT, second order, and Markov features. Six different kernels and four kinds of samplings of SVM are used for classification.…”

Section: Transformmentioning

confidence: 99%

Digital Image Steganalysis: Current Methodologies and Future Challenges

et al. 2022

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With the growing use of the internet and social media, data security has become a major issue. Thus, researchers are focusing on data security techniques such as steganography and steganalysis. Steganography is the approach of concealing the existence of secret messages in digital media for secure transmission. Steganalysis techniques aim to detect the existence of concealed messages and extract them. Digital image steganography and steganalysis techniques are classified into the spatial and transform domains. In this paper, we provide a detailed survey of the state-of-the-art works that have been performed in two-dimensional and three-dimensional image steganalysis. We present the most popular datasets and explain some steganographic methods for embedding hidden data. Steganalysis is a very difficult task due to the lack of information about the characteristics of the cover media that can be exploited to detect hidden messages. Therefore, we review studies performed on image steganalysis in the spatial and transform domains using classical machine learning and deep learning approaches. Additionally, we present open challenges and discuss some directions for future research.

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Moderate embed cross validated and feature reduced Steganalysis using principal component analysis in spatial and transform domain with Support Vector Machine and Support Vector Machine-Particle Swarm Optimization

Shankar

Khalil

Azhakath³

2022

Multimed Tools Appl

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Small Embed Cross-validated JPEG Steganalysis in Spatial and Transform Domain Using SVM

Cited by 3 publications

References 9 publications

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Digital Image Steganalysis: Current Methodologies and Future Challenges

Moderate embed cross validated and feature reduced Steganalysis using principal component analysis in spatial and transform domain with Support Vector Machine and Support Vector Machine-Particle Swarm Optimization

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