A reversible and robust hybrid image steganography framework using radon transform and integer lifting wavelet transform

Image Steganography provides secure communication through hiding confidential information in digital images. To increase security, embedding capacity, and attack resilience, this study proposes a hybrid picture steganography architecture that blends chaotic systems with evolutionary algorithms. The method divides the picture into sub-bands using the Integer Wavelet Transform (IWT), enabling the best possible data embedding. Chaotic systems in logistic maps create pseudo-random sequences, which determine safe sites for message bit embedding, providing randomness as well as immunity against statistical attacks. A genetic algorithm further optimizes the embedding process by searching the search space to identify an optimal chromosome representation, minimizing image distortion while maximizing capacity. The genetic algorithm is put through selection, crossover, and mutation to improve the embedding strategy step by step. With a Peak Signal-Tonoise Ratios (PSNR) of 55.3392 dB, Mean Squared Error (MSE) of 0.3620, Structures Similarities Index (SSIM) of 0.9744, and Visual Information Fidelity (VIF) of 0.9269, experimental comparisons demonstrate the effectiveness of the suggested approach. In comparison to traditional steganography In techniques, the approach also obtains a substantial capacity of concealed information, with 3.67 bits per pixel (BPP). The outcomes point out that the implementation of chaotic sequences along with genetic optimization increases security and attack-resistant resilience without compromising image quality. The method presented offers an improved, effective, and highly secure digital communication steganographic solution.

As the volume of digital data increases, there is an increasing need for effective compression methods to address storage demands. Concurrently, the importance of robust image watermarking for authentication and ownership verification cannot be overstated. This work tackles the dual challenge of optimizing image compression for storage conservation and implementing strong image watermarking for copyright protection. The suggested approach integrates the K‐means clustering compression algorithm to enhance storage efficiency along with a resilient image watermarking technique based on spatial‐domain embedding. We introduce a blind robust watermarking approach that uses zero‐frequency coefficient alteration independently in the spatial domain instead of using the discrete cosine transformation (DCT) to verify the ownership of colored images. To enhance the robustness of the system, we have incorporated two watermarks into the cover image. This precaution ensures that even if one watermark undergoes deterioration due to attacks, authentication can still be assured by recovering the other watermark. Compared to frequency‐domain approaches, our scheme yields better robustness and reduced computing complexity. The average peak signal‐to‐noise ratio (PSNR) for the test images using our approach is above 39 dB with a compression ratio equal to 5.9978, removing up to 83% of the redundancy of the host image. After comparing our approach with several state‐of‐the‐art methods, its robustness is exposed by the values of normalized correlation coefficient (NCC) close to one and bit error rate (BER) values close to zero. Besides, the scheme is able to embed a total of 8192 watermark bits in the host image of size 512 × 512 × 3. Experimental results affirm the effectiveness of the proposed methodology, marking it as a valuable contribution to the domains of image processing and information security.

In telemedicine, digital watermarking is critical for safeguarding patient privacy by securely embedding sensitive Electronic Patient Records (EPR) directly into medical imagery. This work proposes a high-capacity, non-blind, and semi-fragile watermarking scheme leveraging a 64-bit complex-float computational pipeline and the Discrete Orthonormal Stockwell Transform (DOST) combined with Local Binary Pattern (LBP) features. To improve security, the architecture incorporates Arnold scrambling for confusion and chaotic mapping for diffusion-based encryption. In the attack-free scenario, the proposed method significantly outperformed the recent state-of-the-art (SOTA) technique across two MRI datasets. On the TCIA MRI dataset, the proposed method achieved mean values of 79.84 dB for Peak Signal-to-Noise Ratio (PSNR) and 0.9969 for Structural Similarity Index (SSIM), representing average improvements of 111.61% and 4.35%, respectively, over the SOTA baseline (37.73 dB and 0.9553). On the BMIBTD MRI dataset, the proposed method achieved mean values of 65.62 dB for PSNR and 0.9827 for SSIM, reflecting average improvements of 77.54% in PSNR and 11.14% in SSIM compared to the SOTA values (36.96 dB and 0.8842). Furthermore, the proposed method maintained a mean Normalised Correlation Coefficient (NCC) of 1, demonstrating performance parity with the SOTA technique in terms of general robustness. Extensive robustness analysis reveals a selective, tiered sensitivity profile: the scheme exhibits high resilience to stochastic channel noise (NCC≳0.90), a characteristic semi-fragile response to Median filtering (NCC≈0.6-0.8), and high sensitivity to deterministic manipulations such as JPEG compression, Mean filtering, and geometric transformations. Rather than a limitation, this tiered behaviour serves as an inherent integrity-authentication mechanism, where the degradation or destruction of the mantissa-embedded watermark provides immediate detection of unauthorised spatial tampering or resampling post-acquisition. Computational time analysis demonstrates that the proposed method maintains an efficient total mean execution time of 0.2203 seconds, with separate mean processing times of 0.1293 seconds for embedding and 0.0910 seconds for extraction, ensuring rapid performance for real-time clinical applications. The capacity (8.00 bpp) and efficiency (η = 0.0625) of the proposed technique were analysed and found to be high compared to recent SOTA methods. Security analysis confirms the proposed framework's robustness against statistical, differential, and brute-force attacks, achieving superior performance metrics-including high NPCR and information entropy-compared to recent SOTA methods. Beyond MRI datasets, the system demonstrated comparable performance when validated on X-ray and ultrasound scans, confirming the framework's cross-modality consistency. The proposed system provides a significant advancement in balancing high-fidelity diagnostic reconstruction with robust defence against cryptanalytic threats.

Securing or protecting digital content is crucial for business owners and entrepreneurs in today’s technologically advanced world. Through the use of copyright details, such as copyrights incorporated into the data, their digital content can be lawfully protected. This research presents a novel secure digital watermarking method that leverages the All Phase Discrete Cosine Haar Biorthogonal Transform (APDCHBT) for robust watermark embedding into host images. After splitting the host image into blocks; APDCHBT is used to break each block down into its component frequency parts. The watermark is embedded into the frequency coefficients, and the Parrot Optimizer (PO) optimizes scaling variables that determine the embedding strength. By maximizing the Normalized Correlation (NC) for robustness and the Peak Signal-to-Noise Ratio (PSNR) for imperceptibility, this optimizer iteratively finds the ideal scaling factors. The suggested approach exhibits strong resilience to assaults, upholding a harmonious equilibrium between imperceptibility and watermark security. Compared to conventional DWT- and SVD-based watermarking approaches, the proposed method achieves an average PSNR improvement of 12.3%, NC enhancement of 7.8%, and BER reduction by over two orders of magnitude, demonstrating superior resilience to common image processing attacks. The maximum obtained values for the metrics such as PSNR, SSIM, BER, and NC for the proposed research are up to 38 dB, 0.779, 10[Formula: see text], and 0.849.

In this paper, an effective algorithm for providing copyright protection is proposed by using a new embedding strategy for Discrete Wavelet Transform-based video watermarking. Discrete Wavelet Transform (DWT) is applied on the video, to convert the spatial data into frequency domain, having low pass and high pass components. The low frequency component is used for generating the key, by using the watermark image and the binarized Low frequency part (LL) of the video frame. Same procedure is applied on each frame to generate the key for corresponding frame. This generated key should be used at receiver for extracting the watermark which provides copyright protection. Blind watermarking technique is used in this paper which require only key to extract the embedded watermark. The original video is not required during extraction. To criticize the robustness of algorithm, the original watermark image is compared with extracted watermark image after several attacks and their Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NC) and Structural Similarity index (SSIM) are calculated. The experimental results demonstrate that the watermark is invisible and it is robust against the various attacks and addition of noise to the video.

In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Information system security is a crucial aspect in the digital era, especially for protecting sensitive data. One method to enhance data security is steganography, a technique for concealing information within digital media. This study analyzes the effectiveness of the Discrete Cosine Transform (DCT) method in digital image steganography. The research follows several stages, including literature review, system design, DCT implementation using MATLAB/Python, and performance evaluation based on Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Normalized Correlation (NC). Experimental results indicate that the DCT method achieves a high PSNR value (averaging above 40 dB) and a low MSE value, ensuring minimal image distortion after data embedding. Compared to the Least Significant Bit (LSB) method, DCT provides better resistance to visual analysis and is more suitable for images with transform-based compressions, such as JPEG. This study concludes that the DCT method is an effective steganography technique for enhancing information system security, despite challenges related to computational complexity and data embedding capacity. Future research may explore hybrid methods that combine DCT with other techniques to improve efficiency and robustness against various steganographic analysis attacks.

In this paper, a new reversible data hiding scheme has been proposed using lagrange’s interpolating polynomial on interpolated sub-sampled images. First, we generate sub-sampled images from original image and enlarge its size using image interpolation. Now, we convert secret message using lagrange interpolating polynomial and generate new secret message. The new secret message is divided and stored within interleaved pixel of each interpolated sub-sampled images. At the receiver end, new secret message is extracted from interleaved pixel of each sub-sampled stego images and then lagrange’s interpolation is applied to generate original secret message. The security has been enhanced due to the distributive nature of hidden data within multiple images. The original pixels are not effected during data embedding which assure reversibility. The proposed scheme provides average embedding capacity with good visual quality measured by peak signal to noise ratio (PSNR) which is greater than 50 dB. It is observed that the proposed scheme provides better performance than other existing data hiding schemes in terms of data embedding capacity, visual quality and security. We have analyzed our stego images through RS analysis, calculate relative entropy, standard deviation and correlation coefficient of original and stego image to show the robustness under various steganographic attacks.

Quantization parameter based blind watermarking schemes are very popular. These schemes work in spatial domain as well as in various transform domains such as wavelet, discrete cosine, and real oriented wavelet. These schemes utilize one bit (binary) image as a watermark. However, in general, watermarks are any n bit image, where n ≥ 1. Therefore, these schemes are not suitable for the watermarks with n > 1. In this paper, we extend the existing work on blind watermarking schemes for n bits watermark images. The ‘division algorithm’ is used as a key component to develop the extended schemes. The proposed scheme is implemented in spatial domain. Experiments are done on one bit, two bits and eight bits watermark images. The results are investigated qualitatively by visualization, and measured quantitatively by peak-signal-to-noise-ratio (PSNR) and normalized correlation coefficient (NC). The results of the existing and proposed watermarking schemes are same for n = 1. The results of the proposed watermarking scheme are reliable for n > 1. A formula is proven for lower bound on PSNR, which is a key finding in the watermarking community. The formula is verified experimentally.

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.

In the technological era, the primary source of information is in the form of digital data, which has to be secured while storing or transmitting during communication over an unsecured network. Different approaches are used to provide security to digital data, viz. text, audio, image, and video. This paper initially explains the security system such as cryptography, watermarking, and steganography and their comparative analysis based on different characteristics, viz. satisfaction level of objective, type of carrier object and secret information to be used, dependency of security level, and quality assessment parameters. This review article focuses more on steganography methods applied over video. The various methods implemented for video steganography in compressed domain, viz. inter-frame and intra-frame prediction, motion vector estimation, entropy coding (CAVLC and CABAC), and transformed and quantized coefficients of DCT, DST, and DWT, etc. and the methods based on spatial and transform domain for uncompressed video are briefly described. It is followed by the detailed analysis of related work done by various researchers in video steganography and the obtained experimental results. Furthermore, the confidential data hiding in compressed videos are explained using Moving Picture Expert Group (MPEG—1, MPEG—2, MPEG—4), Advanced Video Coding (AVC)/H.264, and High-Efficiency Video Coding (HEVC)/H.265 that includes both spatial and transform domain. This paper summarizes and explains the detailed investigations of numerous techniques of video steganography based on the comprehensive literature survey. The methods used to assess the performance of video steganography are analyzed based on the quality assessment parameters such as imperceptibility; measured by peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM), robustness; measured by bit error rate (BER) and similarity (Sim), and embedding capacity; measured by hiding ratio. The overall review of past literature facilitates to have in-depth knowledge for upgrading the video steganography.

BackgroundThe recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP.MethodsChest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta.ResultsThe image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p < 0.0005), low-dose FBP CT (30.9 ± 3.9 HU, p < 0.0005), and standard-dose FBP CT (16.6 ± 2.3 HU, p < 0.0005).ConclusionMBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT without compromising image quality. With radiation dose reduction of >70%, MBIR can provide equivalent lesion detectability of standard-dose FBP CT.