A Novel Image Steganography Technique Using AES Encryption in DCT Domain

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).

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

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.

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

Image watermarking plays an important role in the field of medical image security and also provides a safe pathway for transfer of medical information through public networks such as internet. In this work, variable pixel and variable bit -medical image watermarking scheme has been proposed to protect the Electronic Patient Record (EPR). The proposed algorithm uses a Linear Feedback Shift Registers (LFSR) to generate pseudo random sequences for randomly selecting the image pixels of 16 bit Digital Imaging and Communications in Medicine (DICOM) image and modified logistic map has been utilized for selecting the bits to achieve adaptive Least Significant Bit (LSB) embedding. Integer Wavelet Transform (IWT) has been applied on original image to decompose it to its sub – bands where EPR has been embedded. The algorithm comprises four phases namely, pixel separation, random pixel selection, variable bit selection and LSB embedding. Proposed watermarking scheme has been achieved a near zero Mean Square Error (MSE) and histogram analysis confirms the imperceptibility of the algorithm. The algorithm requires only 0.235542 S to perform LSB embedding on 512 × 512 DICOM images. Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Structural Similarity Index (SSIM) analyses also have been performed to evaluate the efficiency of the watermarking scheme.

Steganography as a practice is highly useful as it allows information to be concealed within digital images while maintaining visual quality. However, it can be a challenge to balance imperceptibility, capacity, and robustness while ensuring that embedded data remains secure against statistical and Deep Learning (DL)-based steganalysis. Therefore, we propose a new framework that utilizes the BOSSbase dataset for cover images, the USC-SIPI dataset for secreted images, and introduces adaptive image steganography, which combines fused map-based embedding with evolutionary optimization to solve existing problems. The system starts by encrypting the secret image through Blowfish cipher encryption to protect its confidentiality. The model generates a fused map through a combination of entropy and Laplacian-based noise maps from the cover image to determine the most suitable embedding areas. Next, we optimize the process of embedding location selection and ordering using Particle Swarm Optimization (PSO) to maximize both security and image quality. Next, we use priority-guided Least Significant Bits (LSB) substitution in the embedding process to generate an optimized priority map for inserting the encrypted bitstream into the image. The system can function on different cover image sizes, so at [Formula: see text] the resolution is 51-61 dB Peak Signal-to-Noise Ratio (PSNR) and 0.9972-1.0 Structural Similarity Index Measure (SSIM) for stego images at 0.1-1.0 Bits Per Pixel (BPP) with 262,144 bits while maintaining perfect secret reconstruction (SSIM = 1.000) and processing times under 0.25 seconds for embedding and extraction. Security tests demonstrate that the proposed system remains undetectable since the Area Under Curve (AUC) for [Formula: see text] reaches 0.49-0.57 across all payload levels and Regular Singular (RS) statistics remain at 0.59-0.66 between 0.1-1.0 BPP. The proposed model provides effective capacity while maintaining high visual quality and security against DL-based steganalysis, such as that using Convolutional Neural Networks (CNN).

Due to advances in information technology, most of information can be kept electronically, consequently security of information of became a fundamental issues . Steganography is a technique of hiding information in digital media. A large number of commercial steganographic programs use the least significant bit embedding (LSB) as the method of choice for message hiding in 24-bit color images, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, it is proposed to use a LSB embedding algorithm to embed the payload bits of message image into cover image in spatial domain and detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8bit grayscale or color images. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", it can reliably detect messages .By analyzing first order statistic like histogram analysis, entropy it can detect the presence of embedded data. The details of the system and results there of are presented in this paper. (4 pages)

Commentary

The secure communication of information is a major concern over the internet. The information must be protected before transmitting over a communication channel to avoid security violations. In this paper, a new hybrid method called compressed encrypted data embedding (CEDE) is proposed. In CEDE, the secret information is first compressed with Lempel Ziv Welch (LZW) compression algorithm. Then, the compressed secret information is encrypted using the Advanced Encryption Standard (AES) symmetric block cipher. In the last step, the encrypted information is embedded into an image of size 512 × 512 pixels by using image steganography. In the steganographic technique, the compressed and encrypted secret data bits are divided into pairs of two bits and pixels of the cover image are also arranged in four pairs. The four pairs of secret data are compared with the respective four pairs of each cover pixel which leads to sixteen possibilities of matching in between secret data pairs and pairs of cover pixels. The least significant bits (LSBs) of current and imminent pixels are modified according to the matching case number. The proposed technique provides double-folded security and the results show that stego image carries a high capacity of secret data with adequate peak signal to noise ratio (PSNR) and lower mean square error (MSE) when compared with existing methods in the literature.

In this paper, we present a new image steganography technique based on Integer Wavelet Transform (IWT) and Munkres' assignment algorithm which embeds secret image in frequency domain of cover image with high matching quality. IWT is used to transform both cover and secret images from spatial domain to frequency domain, and assignment algorithm is used for best matching between blocks for embedding. We embed the secret image in different coefficients of cover image bands such as horizontal detail, vertical detail and diagonal detail and observe the effect of embedding on the performance of stego image in terms of Peak Signal to Noise Ratio (PSNR). Experimental results depict that stego image and extracted secret image could have high visual quality and they are perceptually similar to their original versions. In addition, this method shows high robustness against six different attacks. Embedding in diagonal detail coefficients gives better PSNR than other coefficients.

Image Steganography is a way to hide the secret information using a cover image. The information in the secret image will be hided in cover image, so that the secret information is no more accessible. In this paper, an image steganography technique has been proposed which is based on block division method and LSB embedding. In block division, the image is divided into blocks of fixed size followed by embedding process. The proposed work ensures the higher quality of the image and promising security. This paper also compares proposed work with existing work on bases of various parameters like PSNR and MSE.

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.

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and comparison to findings from related prior efforts.

In spatial domain image steganography, Least Significant Bits (LSB) of cover image pixels are used to embed a secret message due to minimal distortion and higher payload capacity. In this paper, we have introduced an exclusive-OR (XOR) based encoding of encrypted secret message bits using varying higher-order pixel intensity bits. Encoding and LSB embedding is done block-wise by dividing the cover image into a number of blocks. The secret message is first encrypted using symmetric key cryptography and then encoded those encrypted bits by XORing them with randomly selected higher-order pixel bis of the cover image to obscure the secret bits further. Next, an inversion technique is applied to the encoded bits block-wise to keep the LSB bit changes to a minimum. The stego-key consists of the symmetric encryption key and the encode-key containing parameter settings such as the number_of_blocks, starting_block, start_pixel_offset, block_selection_rule, etc. This stego-key is shared prior to the actual communication using public-key cryptography to ensure the key’s authenticity and integrity. The extraction process does not require the cover image; the stego-image and the stego-key are sufficient. Experimental results show the visual imperceptibility along with improved image quality metrics such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Structural Similarity (SSIM) index in comparison to other well-known techniques. The average PSNR value remains above 51dB, even with 90% of the capacity utilized. The proposed scheme successfully eludes many standard steganalysis attacks such as histogram-based analysis (PDH), chi-square based embed probability test, Regular and Singular groups (RS) analysis, sample pair test, etc. on the tested stego-images.

Steganography, the technology of protecting a secret message by embedding it inside a cover image, continues to be investigated and enhanced as an alternative data protection method. This paper deals with hiding multimedia files in true color RGB cover images with an emphasis on reducing the cover size, increasing hiding capacity and enhancing security of the hidden data. A proposed model (DuoHide) is presented in which a secret multimedia file, regardless of its type, is processed without un-compression, and divided between two cover images of equal size and dimensions. The multimedia file is read as a stream of bytes and split vertically into two parts, one part contains the least significant half-bytes, and the other part contains the most significant half-bytes. The two parts are hidden inside two uncompressed RGB cover images using a least significant 4-bit replacement technique. The resulting dual stego images are expected to be sent separately, through different channels, to avoid capture of both stego files by an adversary. Extraction of the secret file is achieved through merging LSB half-bytes from the two stego files. The extracted file is identical in content and structure with the original secret file. The implemented DuoHide system was evaluated using a set of public multimedia files, images, audios, and videos, of various sizes. The secret file sizes ranged from 5% to about 100% of the cover image's size. The experimental results showed that even at the highest embedding ratio, which is based on the secret-to-cover ratio, there were no perceptible visual differences between cover and stego images. The PNSR value was calculated as PSNR1, for cover1 and stego1, and PSNR2 for cover2 and stego2. The lowest PSNR value was around 31 dB for the highest embedding ratio, which is considered acceptable concerning statistical imperceptibility. The PSNR value increased as the embedding ratio decreased, reaching around 65 Decibel (dB) for the case of 5% secret-to-cover ratio. The integrity of the extracted secret file was verified through a bitwise comparison between original and extracted files, which showed zero differences. The DuoHide model is expected to provide better security for the hidden file, in case an attacker manages to capture one of the stego images and recover the hidden content because the attacker will only get an incomprehensible set of half-byte bits. An additional advantage of using a pair of stego files is that of reducing stego file size by 50%, to avoid problems and limitations of transmitting large files, especially that multimedia files are often large, and they cannot be compressed because they are already compressed. Security of the DuoHide system can further be improved by randomizing storage locations within the two stego images.