Optimasi Steganografi Video Berbasis LSB Multi-Faktor dengan Penyesuaian Bit Adaptif Berdasarkan Kecerahan, Tekstur, dan Stabilitas Gerakan Antar-Frame

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.