1.4 Literature Survey
1.4.1 Steganography Schemes
• Intelligence Agencies: Intelligence agencies use steganography for the safe transmission of confidential data [28].
• Smart Identity cards: In smart identity cards, the user’s personal details are embedded in the photograph itself [27].
• Document Authentication: Steganography allows storing additional in- formation such as owner identity and the document’s subject in the docu- ment itself for verification [27].
• Steganographic File systems: Steganographic file system safely masks given files in a file system such that an eavesdropper will not be able to guess their presence without the corresponding access keys, even though the eavesdropper is thoroughly familiar with the file system and has obtained complete access to it [29].
1.4.1.1 Least Significant Bit (LSB) steganography
Least Significant Bit (LSB) steganography takes advantage of the fact that a small perturbation to a pixel of an image is not perceivable by the HVS. In this method, the least significant bit of the selected pixel is replaced with the message bit. The selection of the pixels for replacement is either successive or pseudo- random. In successive selection, the consecutive pixels of the cover image are replaced with the message bits. In pseudo-random selection, a key is used as a seed for a Pseudo-Random Number Generator (PRNG). ThePRNG generates a random number that specifies a pixel to be modified. Nonetheless, the stego image embedded by the LSB scheme is susceptible to noise and can be easily destroyed by any modification to the stego image. A variety of LSB steganography can be found in [23,30–33].
1.4.1.2 Content-adaptive Steganography
The idea of content-adaptive methods is evolved in recent times. The objective of these methods is to embed a secret message by minimizing a heuristically defined distortion function. The development of these methods is usually driven by the detection performance of steganalysis [34]. In these steganography algorithms, the distortion caused by the embedding modifications relies on the local image content, which results in high embedding modification in the texture regions and low in the smooth regions of the image. Consequently, the name is content- adaptive steganography. An example of the distortion function used by these methods is given in eq. (1.2).
D(X, Y) =
M
X
i=1 N
X
j=1
ρij|Xij −Yij|, (1.2)
where ρij is the costs of modifying pixel Xij to Yij, and X and Y, represent the cover image and the corresponding stego images, respectively, each with size M×N. The embedding of the message to the cover image is done by minimizing
the distortion function,D(X, Y), usingSyndrome-Trellis Codes (STC) [35]. Some of the recent content-adaptive steganography methods are listed below.
HUGO: Highly Undetectable steGO (HUGO) [6] is a secure steganography method that uses high-dimensional image models. HUGO used a steganalysis method called SPAM [13] to compute the high-dimensional features. The dis- tortion function is a weighted sum of differences of SPAM features of cover and corresponding stego images. This design ofHUGOenables the embedding to take place primarily in the textured area and edges.
WOW: Wavelet Obtained Weights (WOW) [5] utilizes a collection of direc- tional high-pass filters to compute the directional residuals and determine the content around each pixel in different directions. This method measures and ag- gregates the embedding impact for each directional residual, thereby forcing the high distortion to the predictable areas in at least one direction (clean edges and smooth regions) and low distortion to the unpredictable regions in every direction (texture regions). The WOWalgorithm is highly adaptive and more secure than the HUGO.
S-UNIWARD: Spatial UNIversal WAvelet Relative Distortion(S-UNIWARD) [15] used a so-called universal distortion function that does not depend on the embedding domain. The distortion is computed as a sum of relative changes of wavelet coefficients. The S-UNIWARD utilizes the wavelet filter bank of the WOW steganography. This method is designed for spatial as well as JPEG do- mains.
HILL: HIgh-Low-Low (HILL) [18] steganography uses the smoothed residual to model the embedding distortion, unlikeWOWandS-UNIWARD, where weighted filter residual difference is used. The HILLalgorithm uses a high-pass filter (Ker- Bohme filter eq. (1.5)) followed by low-pass filters to identify the unpredictable
regions. The HILL steganography showed better performance against SRM ste- ganalysis as compared to HUGO,WOW, and S-UNIWARD.
CMD steganography: Clustering Modification Directions (CMD) steganog- raphy [36] works as a wrapper for steganography schemes by dynamically varying the embedding costs to attain a lower detection rate. The dynamic cost function allows the algorithm to take advantage of mutual embeddings and clustering of similar distortions. This clustering ensures that the embeddings in neighboring pixels are of similar nature (±1), which increases the steganographic security.
The CMD approach is very general and robust and can be applied to any exist- ing additive steganographic algorithm, such asWOW[5],S-UNIWARD[15], and HILL[18]. When the wrapping ofCMDis applied over any of the aforementioned steganography schemes, the steganalytic performance of SRM [14] degrades by
∼5%.
1.4.1.3 Steganography Using GAN
With the recent evolution of deep-learning, researchers have explored various as- pects of deep-learning; specifically, Generative Adversarial Network (GAN) [37], to solve steganographic problems, such as designing embedding distortion func- tions, generating secure cover images for embedding, hiding the message in im- ages, etc.
Embedding Distortion Learning Using GAN: Tang et al. proposed the ASDL-GAN [38] for automatic learning of steganographic distortion function.
ASDL-GAN uses a generator network with 25 layers of the CNN model, which predicts the embedding change probabilities for every cover image pixel. The predicted probabilities are used as embedding distortion. The XuNet [39] is used as the discriminator of ASDL-GAN to ensure the undetectability against state- of-the-art detectors. However, the performance of the ASDL-GAN could not compete with that of the handcrafted steganography scheme S-UNIWARD. In
a similar direction, Yang et al. proposed a UT-GAN [40] to predict embedding change probability from a given cover image. The UT-GAN uses U-Net [41] ar- chitecture as the generator and modified XuNet architecture as the discriminator.
The authors showed that the UT-GAN outperformed the steganography schemes ASDL-GAN, S-UNIWARD, HILL, and MiPOD.
Secure Cover Generation Using GAN: Volkhonskiy et al. [42] devised a GAN-based model calledSGAN to generate container/cover images from a noise distribution. The generated images are embedded using ±1 embedding to obtain corresponding stego images. Haichao et al. [43] proposed a model similar to SGAN [42], namely SSGAN. The SSGAN model uses Wasserstein distance as a loss function [44] for training. The steganalyzer used in SSGAN is well known (one of the early CNN-based steganalyzers) GNCNN [2]. The authors showed that the SSGAN generated cover is more secured than SGAN. The cover images produced by [42,43] are more secure than the ordinary cover images. However, the generated images can be easily suspected as they look unrealistic. Therefore, these images may not be used as the cover image in practice.
Information Hiding Within an Image Using GAN: Hayes and Danezis [45] proposed a generative adversarial model comprised of a generator, a stegan- alyzer, and an extractor. The generator hides an n-bit binary message within a cover image, and the extractor retrieves the secret message from the stego image.
Baluja [20] used an adversarial training approach to hide one image in another image of the same size. In [21], Baluja extended this approach to hide multiple images in an image. This approach only uses MSE loss for both embedder and extractor and has not used any discriminator to impose undetectability. Zhang et al. proposed ISGAN [46] to hide and recover a grayscale image within the Y-channel of an image using GAN. ISGAN used SSIM and MS-SSIM loss for training to improve the imperceptibility. Zhu et al. [47] used a GAN-based net- work to embed and extract a bit string encoded message in the image. Zhang et
al.[48] introduced SteganoGAN to hide/extract binary data to/from images. The methods [47,48] have not used extractor loss in training, resulting in improper embedding and might show visual artifacts in generated stego images.