Hiding Encrypted Multiple Secret Images in a Cover Image

Guttikonda, Prashanti; Cherukuri, Hemanthi; Mundukur, Nirupama Bhat

doi:10.1007/978-981-10-6319-0_8

Cited by 2 publications

(11 citation statements)

References 5 publications

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“…Moreover, PSNR values over 30 dB are considered good (Gutub and Shaarani, 2020;Zhang et al, 2013;Liu and Liao, 2008). Further, our scheme, like most of the other competitive schemes (not all; please see (Guttikonda et al, 2018)), is resistant to steganographic attacks. Finally, we are the first ones to embed four secret images in a cover image.…”

Section: Security Analysismentioning

confidence: 75%

“…Here, three binary images are embedded into a grayscale cover image. The authors in (Guttikonda et al, 2018) achieve an embedding capacity of 2 bpp while we achieve an embedding capacity of 6 bpp. When comparing the stegoimage and the corresponding cover image, (Guttikonda et al, 2018) achieve a PSNR value of 46.36 dB while we achieve a PSNR value of 38.12 dB.…”

Section: Referencementioning

confidence: 92%

“…Technique Secret images Cover image (Baluja, 2019) A modified version of Least Significant Bits (LSB) with deep neural networks 2 color color (Gutub and Shaarani, 2020) LSB 2 color color (Guttikonda et al, 2018) LSB 3 binary grayscale and color…”

Section: Referencementioning

confidence: 99%

“…Until now, in most works, researchers have embedded two secret images in a cover image. Some people have looked at embedding three secret images but this is (Gutub and Shaarani, 2020) DWT and SVD 2 color color rare (Guttikonda et al, 2018). Here, not just the number of secret images but the total size of the secret images is also important.…”

Section: Referencementioning

confidence: 99%

“…For the case of embedding three secret images in a cover image, we compare with (Guttikonda et al, 2018). Here, three binary images are embedded into a grayscale cover image.…”

Section: Referencementioning

confidence: 99%

See 4 more Smart Citations

SABMIS: Sparse approximation based blind multi-image steganography scheme

Agrawal¹,

Ahuja²,

Steinbach³

et al. 2021

Preprint

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Steganography is a technique of hiding secret data in some unsuspected cover media so that it is visually imperceptible. The secret data as well as the cover media may be text or multimedia. Image steganography, where the cover media is an image, is one of the most commonly used schemes. Here, we focus on image steganography where the hidden data is also an image. Specifically, we embed grayscale secret images into a grayscale cover image, which is considered to be a challenging problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image and ensuring that the stego-image is resistant to steganographic attacks. Our proposed scheme involves use of sparse approximation and our novel embedding rule, which helps to increase the embedding capacity and adds a layer of security. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. This method has a fast convergence, is easy to implement, and also is extensively used in image processing. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together helps us to embed up to four secret images into one cover image (instead of the common embedding of two secret images) and forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images, and evaluate the embedding capacity, Peak Signal-to-Noise Ratio (PSNR) value, mean Structural Similarity (MSSIM) index, Normalized Cross-Correlation (NCC) coefficients, entropy, and Normalized Absolute Error (NAE). We obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. These embedding capacities are higher than all the embedding capacities obtained in the literature until now. Further, there is very little deterioration in the quality of the stego-image as compared to its corresponding cover image (measured by above metrics). The quality of the original secret images and their corresponding extracted secret images is also almost the same. Further, due to our algorithmic design, our scheme is resistant to steganographic attacks as well.

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Section: Security Analysismentioning

confidence: 75%

Section: Referencementioning

confidence: 92%

Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

“…For the case of embedding three secret images in a cover image, we compare with (Guttikonda et al, 2018). Here, three binary images are embedded into a grayscale cover image.…”

Section: Referencementioning

confidence: 99%

See 3 more Smart Citations

SABMIS: Sparse approximation based blind multi-image steganography scheme

Agrawal¹,

Ahuja²,

Steinbach³

et al. 2021

Preprint

View full text Add to dashboard Cite

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SABMIS: sparse approximation based blind multi-image steganography scheme

Agrawal¹,

Ahuja

Steinbach³

et al. 2022

PeerJ Computer Science

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We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet.

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Hiding Encrypted Multiple Secret Images in a Cover Image

Cited by 2 publications

References 5 publications

SABMIS: Sparse approximation based blind multi-image steganography scheme

SABMIS: Sparse approximation based blind multi-image steganography scheme

SABMIS: sparse approximation based blind multi-image steganography scheme

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