Image-to-image steganography, embedding secret information within images while preserving visual quality, has become essential due to growing demands for secure and efficient digital communication. Traditional methods often struggle to achieve high embedding capacity without sacrificing imperceptibility. Recent advancements in deep learning have offered promising solutions by enabling more complex data embedding strategies. In this paper, we propose AttenHideNet, a novel deep learning-based steganography method leveraging a lightweight U-Net architecture (<1.2 million parameters) combined with soft attention mechanisms. By utilizing the YUV color space instead of RGB, our method significantly improves embedding efficiency, capacity, and visual imperceptibility. AttenHideNet achieves an embedding capacity of up to 24 bits per pixel (bpp) while maintaining high visual quality. The soft attention mechanism dynamically identifies and prioritizes embedding in less perceptually sensitive image regions. Experimental results on benchmark datasets demonstrate that AttenHideNet achieves superior visual quality (PSNR up to 52.67 dB) compared to state-of-the-art methods, with low latency (18 ms/image) and minimal memory usage (4.11 MB), making it suitable for real-time applications. Despite these advantages, the method shows limited robustness under firm JPEG compression and geometric transformations, highlighting essential directions for future research.

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).

ElGamal cryptosystem is one of the well-known public-key algorithms for its ability to generate different ciphertexts for the same plaintext on successive runs. However, this algorithm results in a ciphertext occupying a larger memory space than its plaintext due to its encryption nature. As a result, it is pretty infeasible to use data that require their encrypted form to have the same size, such as image data. To overcome this issue, we propose an enhanced ElGamal cryptosystem that can be used for any given digital data message, including image, text, and video. The proposed approach mainly tests image data, consisting of three stages: key pair generation, image encryption, and image decryption. First, we generate as many random bytes as required for encrypting or decrypting images using the sender or receiver's public key information. Then, we use an XOR operation between each pixel in the image and each randomly generated byte to obtain the encrypted or decrypted image. Experimental results revealed that the proposed approach gives excellent results in various evaluation metrics tested on four different color images.

Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.

Accepting Augmented Reality (AR) technologies to add to our observation and assist our understanding, hearing, and touch our surroundings in novel and augmented habits. AR determination maintenance in playing field such as edification, preservation, proposal and investigation, to designation but a few. Designates the sports ground of AR, together with a momentary characterization and improvement olden times, the allowing knowledge and their physical appearance. It surveys the formal of the art by rereading about recent submissions of AR technology with some recognized margins on the subject of anthropological features in the use of AR methods that inventers will need to astound. In this paper, an AR technique is presented, as a review from 2017 until 2018. Furthermore, we have stated the best technique of AR, and table of summarization for all reviewed articles.

Information hiding has brought a lot of attention in recent years. In much of this research tasks, different techniques were considered where data could be prevented from stealing. This will avoid any possible risk it may happen to our data. Due to the challenges involved in designing different security algorithms using hiding based aspects such as the nature of wireless communication, literature has proposed new algorithms in order to increase the security of the data. Steganography has been used to hide data using media cover to deal with the right target. However, these techniques have different issues regarding the visual quality, capacity, and robustness. Based on this, we proposed a new method of steganography to make the balance between all factors by using the feature points. The proposed method also improves the layer of security for the hidden data due to applying Arnold Cat algorithm on secret message prior to the embedding stage.