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The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices. Abstract: Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices.

This research aims to enhance the imperceptibility of secret messages in video steganography to prevent detection by third parties. Unlike cryptography, which focuses on securing the message content, this method seeks to conceal the very existence of the message by maintaining a visual quality nearly identical to the original video. The proposed approach utilizes an adaptive multi-bit scheme based on the Least Significant Bit (LSB) technique, which intelligently analyzes the local characteristics of each frame, including brightness, texture, and motion. This strategy allows for higher data insertion in areas with high visual tolerance while limiting bits in sensitive areas to prevent distortion. Evaluation results using PSNR and SSIM metrics indicate that while increasing message capacity (from 1-bit to 3-bit) reduces the PSNR from 51.80 dB to 39.44 dB, the method remains highly effective in preserving visual integrity. Overall, this technique proves to be more secure and superior to traditional LSB in balancing storage capacity with high-quality video output.

Steganography is a technique for hiding secret data within digital media such as images, audio, and video without causing noticeable visual changes. In video media, this technique offers advantages because each frame can be utilized dynamically, resulting in a larger data embedding capacity. However, conventional methods such as fixed-number Least Significant Bit (LSB) embedding still face limitations in balancing visual quality, embedding capacity, and resistance to compression or noise. To address these challenges, this study proposes an Adaptive Video Steganography Method based on Multi-Bit LSB that employs brightness, texture, and motion analysis for each frame to determine the number of embedding bits adaptively. The system adjusts the embedding capacity according to the local characteristics of the video: areas with high texture or rapid motion are assigned more bits, while static or low-texture areas use fewer bits to preserve visual quality. After the embedding process, the video quality is evaluated using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measurement) to assess its similarity to the original video. Experimental results show a PSNR value of 45.86 dB and an SSIM value of 0.9441, Thus, the proposed adaptive method proves to be efficient, robust against disturbances, and capable of maintaining data security without compromising visual quality, making it highly suitable for implementation in multimedia-based information security systems.

Steganography is a method to hide confidential messages in digital media so that they are not detected by unauthorized parties. Unlike cryptography which protects the content of messages through encryption, steganography hides the message itself. One popular technique is the Least Significant Bit (LSB), which replaces the least important bit on the pixel with a secret message bit. However, conventional LSB methods such as 1-bit or 3-bit have limitations due to the compromise between insertion capacity and visual quality of the media. This study proposes an LSB-based video steganography method with an adaptive multi-bit embedding approach. This technique determines the number and position of bits that are dynamically inserted based on the local brightness and texture levels of each video frame, with Laplacian operators used to analyze both high and low textured areas. The process includes frame and audio extraction, frame-by-frame embedding, inserted video reconstruction, and decoding using video cover references. The evaluation was carried out quantitatively using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, as well as qualitatively through visual comparison. The results showed that the adaptive multi-bit method was able to maintain visual quality with a PSNR of 45.23 dB and SSIM of 0.9424, and increased the insertion capacity by up to 2–3 times compared to the 1-bit adaptive method. Thus, this approach effectively balances imperceptibility and insertion capacity on dynamic video steganography systems.  

The Markov Chain-based linguistic steganography algorithm can effectively hide information within human-like cover text, but it is highly limited in processing speed. A traditional implementation relying on Huffman tree-based encoding mainly suffers from slow processing due to the computational overhead of building the tree itself. To address this issue, this study proposes an enhanced algorithm using binary indexing for constant time complexity. The results were experimentally calculated using models of varying state sizes derived from the same text corpus as a control variable. Perplexity analysis was also employed to evaluate imperceptibility and ensure there were no drawbacks to the cover media’s integrity. The results indicate that the enhanced algorithm improves processing speed by up to 54 times across all state sizes without compromising imperceptibility. This establishes that the enhancements yielded a significantly faster processing speed for the existing algorithm while remaining secure in its concealment.  In practice, the algorithm was applied in legal document storage to strengthen its security.

In the daily use of technology, humans cannot be separated from the internet as a need to exchange information. People at this time spend more time in front of computer screens, laptops, even smartphones to find out information, send data to some of their friends. One of the information that is often sought or sent is a file. Apart from that, image files are files that are much searched for and sent, and many also contain important information in them. Image file security is of course very important so that unauthorized parties do not hack or manipulate information from the image. There is a way to secure information so that it does not leak to unauthorized parties, namely by using cryptography and steganography. By combining these two methods, you can maintain the confidentiality and security of a file, especially image files. In this study the algorithm and method used is the AES cryptographic algorithm (Advanced Encryption Standard) 256 and the LSB (Least Significant Bit) steganography method. Data integrity needs to be tested to ensure that the encryption and decryption processes are running properly. Data integrity testing uses the SHA-1 method. Likewise, the image quality after insertion will experience a decrease in quality. To evaluate this, it is necessary to test using the PSNR method. From the results of data integrity testing by comparing the hash value of the decrypted image file with the original image file, there is no difference. So this shows that the encryption and decryption process was successful. While in testing using the PSNR method, the average PSNR value is 44.14086 dB and with an average error value of 2.830403 dB, which means there is a small decrease in quality. From the implementation and testing results, it can be concluded that the AES 256 cryptographic algorithm and the steganography method LSB can be implemented in maintaining the confidentiality and security of secret messages.