Reversible data hiding scheme using sub-sampled image exploiting Lagrange’s interpolating polynomial

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).

In this paper, a reversible data hiding scheme has been proposed which is based on correlation of subsample images. The proposed method modifies the blocks of sub-sampled image to prepare vacant positions for data embedding. The PSNR of the stego image produced by the proposed method is guaranteed to be above 47.5 dB, while the embedding capacity is at least, almost 6.5 times higher than that of the Kim et al. techniques with the same PSNR. This technique has the capability to control the capacity-PSNR. Experimental results support that the proposed method exploits the correlation of blocked sub-sampled image outperforms the prior works in terms of larger capacity and stego image quality. On various test images, the authors demonstrate the validity of the proposed method by comparing it with other existing reversible data hiding algorithms.

Data hiding is a technique that embeds a cluster of secret messages into the original image. The image with secret messages can be distributed on the Internet while the message embedded would not be easily discovered by a third party. In this way, the secret message can be well protected. At the same time, an image might be made with complex and smooth textures. If changes are made on complex textures, it is less easy for human eyes to discern the differences in the image; but if changes are made on smooth textures, the changes are easier to be discerned by human eyes. This paper proposes a data hiding method based on reduplicated exploiting modification direction (REMD), image interpolation, and canny edge detection. It aims at fulfilling two goals. First, conduct difference-embedding on the image's feature information, distribute the image, and use image interpolation to accomplish reversibility. Second, check the pixels that are located at the edge and insert different data payload according to the application demands. This allows users to flexibly adjust data payload embedded into the image's edge pixels according to their practical conditions, effectively considering both the image quality and payload. The experimental results demonstrate that the proposed method can achieve the data payload of 3.01bpp, so this is a reversible hiding technique with a very high embedding capacity. In the mean time, the average image quality is kept at an acceptable level, about 33?±?1 dB.

This paper presents a novel reversible data hiding method, which can recover the original image content without distortion from the stego images after the secret data have been extracted. The method utilizes the original cover image to finally generate two visually similar stego images by embedding additional data. During the data embedding process, the pixels in the first stego image are utilized to embed secret data by applying the proposed prefix matching technique, which exploits each of the gray values to carry one or two bits only with ± 1 operation. Pixels in the second stego image are adaptively modified to carry rest secret data by referring to the first stego image using the directed LSB embedding. For the recipient, the additional data can be extracted from the two stego images with the DH key, and the original cover image can be recovered with the location map. If the cover image has no boundary gray-values, one can always recover the cover image from the two stego images without the aid of any extra information. Experiments show that the proposed method can achieve desirable embedding capacity and maintain a high level of image quality.

A reversible data hiding scheme provides security for a secret message by embedding it in a cover image. After extracting the secret message at the receiver, the scheme restores the original message and the cover image without any distortion. However, data transmitted by the reversible data hiding scheme is vulnerable to the noise existing on the communications channel. A reversible data hiding scheme using turbo code is proposed in this paper to address that problem. The proposed reversible data hiding integrates modified matrix embedding, turbo code, and syndrome embedding to provide improved embedding capacity, security, and error correction. Modified matrix embedding hides the secret message in the cover image. The resulting stego-image is then encrypted and encoded by the turbo code. Turbo code deploys a secret key interleaver that shuffles the incoming bits using elliptic curve arithmetic and a secret key. The exact recovery of the cover image and the secret message by the receiver depends on the user-specific secret key. Modified matrix embedding hides a large number of secret message bits, which distorts the stegoimage. However, encrypting the stego-image with the secret key interleaver makes the embedding invisible. The proposed syndrome embedding transmits the locations of the modified bits in the cover image, and enables the receiver to reverse the effects of embedding. Simulation results with the proposed scheme demonstrate a high embedding capacity and improved error correction performance of 10-4 for an additive white Gaussian noise (AWGN) channel at an SNR of 2dB. It also successfully recovers the exact cover image and the secret message, compared to existing schemes such as matrix embedding and reversible data hiding in the encrypted domain. Moreover, the results depict high resistance to brute force attacks, statistical attacks, noise attacks, and cropping attacks.

One of the container media that is available and popular is the Joint Photographic Experts Group (JPEG) format image. This article aims to determine the effect of Quality Factor on the secret message capacity of JPEG image steganography and stego image quality. The quality of an image can actually be seen subjectively with the human eye, but this is relative between each individual. Because the assessment of the human eye varies from person to person. In addition, the effect of Quality Factor on secret message capacity is not yet known whether it has an impact. Therefore, in this study the Quality Factor is used to objectively see the secret message capacity of the JPEG image steganography and the quality of the stego image. The parameter used to determine the quality of an image is the Peak Signal to Noise Ratio (PSNR). PSNR will compare the quality of the original image (before steganography) with the stego image. The test results show that the Q Factor effect can affect the secret message capacity of the JPEG image steganography and the stego image quality. The bigger the Q Factor, the more the message capacity is generated. The greater the Q factor, the better the quality of the resulting stego image.

A coding layer robust reversible watermarking algorithm for digital image in multi-antenna system

In this paper, a reversible data hiding scheme based on histogram-shifting of prediction errors (HSPE) is proposed. Two-stage structures, the prediction stage and the error modification stage, are employed in our scheme. In the prediction stage, value of each pixel is predicted, and the error of the predicted value is obtained. In the error modification stage, histogram-shifting technique is used to prepare vacant positions for embedding data. The peak signal-to-noise ratio (PSNR) of the stego image produced by HSPE is guaranteed to be above 48 dB, while the embedding capacity is, in average, 4.74 times higher than that of the well known Ni et al.psilas technique with the same PSNR. Besides, the stego image quality produced by HSPE gains 7.99 dB higher than that of Ni et al.psilas method under the same embedding capacity. Experimental results indicate that the proposed data hiding scheme outperforms the prior works not only in terms of larger payload, but also in terms of stego image quality.

The basic requirements of the information hiding system are good visual quality, high hiding performance and high steganography security. Reversible steganography can reconstruct an original image without loss from the stego image after extracting the hidden message. The traditional methods modify the value of the image to embed the message. The differences between the proposed method and existing algorithms is that: this paper proposes an algorithm based on non-zero number locations. A block selection strategy based on the number of zero coefficients in each 8 × 8 block is proposed, which can be utilized to select DCT coefficients for data hiding in JPEG images. We first arrange the bottom right corner of each 8×8 DCT block into a one-dimensional vector. Then the secret message is recorded in a non-zero number position within the vector. Finally, the original data and the secret message are synthesized into reversible data, which is embedded into the stego image. It can be used to recover the original carrier DCT coefficients. Experimental results show that the PSNR value of the proposed method is higher than the traditional algorithm. The algorithm in this paper has high security and concealment.

The multi-bit image steganography (Paul et al. Multimedia Tools and Applications 76(5):7445–7471, 2017) proposed by Paul et al. provides high embedding capacity, and adapts the embedding position of messages to image content. This paper investigates the security and robustness of multi-bit steganography. In detection resistant experiment, we embed secret messages into cover images with multi-bit image steganography to get corresponding stego images, and adopt a more popular steganalyzer (SRM) to extract features of cover images and stego images. The ensemble classifier is trained with default settings. The experimental results show detection resistance performance of multi-bit image steganography is poor. In robustness test, we embed secret messages into cover images with multi-bit image steganography to get stego images, and attack the stego images with Stirmark. Then, we extract the secret messages from the attacked stego images. The experimental results demonstrate that we can not recover the secret messages from the attacked stego images. Therefore, there are flaws in detection resistant test and robustness test of multi-bit image steganography.

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

Prediction of pleural invasion in lung cancer is crucial in planning appropriate operating procedures and can be assessed using dynamic chest radiography (DCR). However, this assessment is negatively affected by rib shadows in conventional images. The purpose of this study was to develop a deep learning-based bone suppression technique for DCR in various projection directions. Twenty breathing XCAT phantoms with lung tumors and a pair of phantoms consisting only of bone structures were generated. The XCAT phantoms were virtually projected from six directions, resulting in 54000 multidirectional chest X-ray images and were used for training a pix2pix model to estimate bone images from original images. Bone suppression (BS) images were created by subtracting the bone images from the original images and then evaluated based on the peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and Fréchet Video Distance (FVD). Clinical cases were processed using the trained model as well. Furthermore, lung tumors on the original and BS images were tracked using OpenCV template matching, and the tracking accuracy was compared. The PSNR, SSIM, and FVD were 0.9966, 52.20 and 136.9, respectively. In the visual evaluation, we confirmed the effect of BS without temporal fluctuation of pixel values in both the resulting images of the XCAT phantom and real patients. Furthermore, precise tracking of the targeted tumor was achieved on the resulting BS images even in oblique directions, without any interruption from the rib shadows. These results indicate that our proposed method can effectively reduce bone shadows as well as temporal variations in the effect of bone suppression.

R261, Jana et al. proposed the partial reversible data hiding (PRDH) based on (7, 4) Hamming code, which deals with three images: the original image (OI), the cover image (CI), and the stego image (SI). The CI is obtained by slightly modifying OI. After that, one can embed secret into CI to generate SI. The so-called partial reversible feature is that we can reconstruct the CI faultlessly, which is almost the same to the OI. However, in Jana et al.'s PRDH, the authors only adjust redundant bits by using (7, 4) Hamming code with even (or odd) parity. This simple approach may modify 3 redundant bits at most in 7 least significant bit (LSBs) to obtain CI from OI, and this degrades the CI quality seriously. In this paper, we construct two schemes: the proposed PRDH and the modified PRDH. The proposed PRDH enhances the partial reversible property that improves the visual quality of CI. And, the modified PRDH enhances the proposed PRDH to achieve the high embedding capacity. In addition, we also point out two inaccurate descriptions in Jana et al.'s PRDH about position of embedded secret to more simplify the procedure of embedding secret.

Data hiding is a technique that embeds a secret message into a cover medium and transfers the hidden information in the secret message to the recipient. In the past, several data hiding methods based on magic matrix have used various geometrical shapes to transmit secret data. The embedding capacity achieved in these methods was often limited due to simple geometrical layouts. This paper proposes a data hiding scheme based on a double-layer octagon-shaped shell matrix. Compared to previous octagon-shaped data hiding methods, the proposed method embeds a total of 7 bits in each pixel pair, reaching an embedding capacity of 3.5 bits per pixel (bpp). Experimental results show that the proposed scheme has a higher embedding capacity compared to other irreversible data hiding schemes. Using the proposed method, it is possible to maintain the Peak Signal to Noise Ratio (PSNR) within an acceptable range with the embedding time less than 2 s.

The use of digital images has become very common because of the rapid increase of the internet over time. Moving digital images over the internet is easy, but keeping ownership is complex, and serious issues have emerged. Forgery, fraud, and pirating of this content are rising. Different techniques used to protect images, like watermarking and steganography, but these methods are not enough toprotect. So, providing new techniques is essential for protecting image ownership. We have proposed a fusion method of steganography and watermarking in this work. First, the secret message is encoded within the original image using the LSB technique to obtain the stego image. Secondly, the watermarking process is applied on the stego image using text watermarking or image watermarking to provide stego-watermarked-image. The proposed fusion watermarking and steganography method is very useful for protecting image ownership over insecure communication channels. An attacker cannot get the desired watermarked image from the stego-watermarked-image without knowing the secret message hiding inside it using the LSB technique. The proposed method is efficient, simple and secure; it provides significant protection for image ownership.