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Simulation results of the proposed digital image watermarking model for a line images, b fine detail images, c bright color images, d dark images, e smooth images, and f classic images
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Digital image watermarking has been considered a major requirement in diverse applications like broadcast monitoring, data authentication, and identification of ownership and Internet. Generally, authentication of image consists of traditional digital signature, semi‐fragile and fragile watermarking, cryptography and etc. It ensures integrity and a...
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One method for securing multimedia data, and images in particular, from malicious attacks is watermarking, which involves adding a signature to the images. Traditional watermarking techniques, however, have drawbacks when used on sensitive images like those used in medicine. A reliable and blind watermarking method is suggested in this work to secu...
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Citations
... Image encryption is the foundation of contemporary digital networks since images make up the vast majority of digital data transport. Metaheuristics [1,2], DNA encoding [3,4], Chaotic Maps [5,6], Cellular Automata [7][8][9], and other methods are some of the frequently used methods for image encryption. ...
Image encryption plays a vital role in securing sensitive visual information during transmission and storage. This paper proposes SOCIET, a novel lightweight image encryption technique that combines Second-Order Cellular Automata (SOCA) and a chaotic map. The SOCA model is utilized to generate pseudo-random sequences, which are used for pixel shuffling and bitwise XOR operations with the image pixels. The chaotic map is employed to randomly generate the key image from an arbitrary initial vector, enhancing the security of the encryption process. The proposed scheme achieves a high encryption speed due to the lightweight nature of the SOCA model, making it suitable for real-time applications. Extensive experimental results demonstrate the effectiveness and robustness of the proposed scheme against various attacks, including various statistical analyses and differential attacks.Furthermore, the scheme achieved improvements of 9.47% and 16.5% in terms of MSE and PSNR values compared to the other existing schemes. Moreover, the encryption scheme maintains a low computational complexity, making it suitable for resource-constrained devices.
... Message restoration in the offered image watermarking method is used for restoring the actual image from the watermarked image [31]. The image is then transferred to the destination via a network connection after the secret message has been inserted. ...
Accessing digital media has become quite a simple result of the fast rise of multimedia in the case of network technology. As a result, safeguarding intellectual property necessitates a greater focus on image watermarking. Distinct image watermarking systems have been introduced for this purpose; however, they have limitations with transparency and robustness. In the sphere of digital watermarking, multimedia copyright protection plays a critical part. The practice of extracting and embedding a watermark discreetly on a carrier image is called digital image watermarking. Digital watermarking is successful in securing digital data; it also has sparked a lot of study attention nowadays. Deep learning networks combined with wavelet-oriented approaches for image watermarking have gotten a lot of interest these days. Conventional watermarking techniques, on the other hand, cannot provide blindness, resilience, and automated extraction and embedding all at the same time. In this circumstance, this paper motivates to offer an improved approach for generating watermarked images with elevated invisibility using deep learning with a novel wavelet-based technique. Initially, after gathering the data, image griding is performed to partition the images into grids, thus making the image suitable for efficient feature extraction. Then, the two techniques named Deep feature extraction by Convolutional Neural Network and Neighboring-based features are extracted. Using these features, the Modified Deep Neural Network (MDNN) is used for choosing the regions for embedding the watermark. Here, the training algorithm of DNN is optimized by the Squirrel Search Algorithm (SSA) and Grey Wolf Optimization (GWO), known as Squirrel Search–Grey Wolf Optimization (SS–GWO). Once the regions are selected, watermark embedding is performed by the Adaptive Discrete Wavelet Transform (ADWT) with filter coefficient optimization by the same SS–GWO based on a newly derived fitness function. Accordingly, the message extraction is achieved using the same ADWT with the embedding key. Throughout the results, the mean of SS–GWO-MDNN + ADWT is 31.25%, 10.53%, 16.67%, and 23.53% improved than SSA-MDNN + ADWT, GWO-MDNN + ADWT, PSO-MDNN + ADWT, and JA-MDNN + ADWT regarding Gaussian filtering attack for dataset 3. The simulation findings, and a comparison of prior approaches, suggest that the developed mode has a considerable increase in image processing attack robustness, making it ideal for copyright protection applications.
... Optimization is aimed at finding the minimum values of the target vector. Soppari and Chandra (2022) propose a blind digital image watermarking model based on the multi-objective hybrid metaheuristic-based clustering approach. Metaheuristic (72) ...
The digital content exchange on the Internet is associated with information security risks. Hiding data in digital images is a promising direction in data protection and is an alternative to cryptographic methods. Steganography algorithms create covert communication channels and protect the confidentiality of messages embedded in cover images. Watermarking algorithms embed invisible marks in images for further image authentication and proof of the authorship. The main difficulty in the development of data hiding schemes is to achieve a balance between indicators of embedding quality, including imperceptibility, capacity, and robustness. An effective approach to solving this problem is the use of metaheuristic optimization algorithms, such as genetic algorithm, particle swarm optimization, artificial bee colony, and others. In this paper, we present an overview of data hiding techniques based on metaheuristic optimization. We review and analyze image steganography and image watermarking schemes over the past 6 years. We propose three levels of research classification: embedding purpose level, optimization purpose level, and level of metaheuristics. The results demonstrate the high relevance of using metaheuristic optimization in the development of new data hiding algorithms. Based on the results of the review, we formulate the main problems of this scientific field and suggest promising areas for further research.
The active use of the Internet and multimedia content has recently escalated copyright violations. Digital content, especially images and videos are subject to vulnerable attacks. It is also possible that an attacker might remove the watermark from the original image. Therefore, the copyright of digital images must be secured to prevent them from being inappropriately misused. This paper proposes an Enhanced Chimp Optimization algorithm based on Deep Fusion Convolutional Neural Network (ECO-DFCNN) for robust watermarking. The proposed framework consists of an embedding and extraction network to embed and extract the watermark. The octave convolutional model introduced in the embedding network captures various features and decreases spatial redundancy. In addition, the ECO algorithm is introduced to overcome the trade-off between robustness and imperceptibility by determining the optimal strength factor. The pyramid feature extraction module in the extraction network extracts the local features and the dilated convolutions minimize the model parameters. The proposed ECO-DFCNN method is tested against various attacks such as histogram equalization, compression, cropping, scaling, blurring, and median filtering. The proposed ECO-DFCNN method is evaluated and the performance is determined by comparing the obtained results with the existing watermarking techniques. The results show that the proposed ECO-DFCNN watermarking method is robust against various attacks while maintaining excellent imperceptibility with a high Peak Signal-to-Noise Ratio of 54.64 dB, Normalized Correlation of 0.98 and Structural Similarity Index Measure of 0.97, and low Bit Error Rate of 0.038.
