Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms

2025-08-19

AI Summary

To address the challenge of region-of-interest (ROI) selection in video steganography—where simultaneous optimization of embedding capacity, visual fidelity, and security remains difficult—this paper proposes a genetic algorithm (GA)-based adaptive ROI optimization method. First, secret data are encrypted using AES; then, GA dynamically identifies optimal embedding regions across video frames—characterized by rich texture and low motion—to enable high-capacity, perceptually imperceptible data hiding. The method ensures real-time performance while significantly enhancing steganographic robustness and security. Experimental results show PSNR values of 64–75 dB, indicating near-lossless video quality; embedding capacity reaches up to 10% of the original video data size; and encoding/decoding latency remains low, supporting real-time applications. The core innovation lies in the co-modeling of evolutionary optimization and encrypted embedding, achieving, for the first time, adaptive ROI selection jointly optimized for visual perception and security.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Evolutionary Computation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video quality while maintaining high efficiency. This paper proposes a new method to video steganography, which involves utilizing a Genetic Algorithm (GA) for identifying the Region of Interest (ROI) in the cover video. The ROI is the area in the video that is the most suitable for data embedding. The secret data is encrypted using the Advanced Encryption Standard (AES), which is a widely accepted encryption standard, before being embedded into the cover video, utilizing up to 10% of the cover video. This process ensures the security and confidentiality of the embedded data. The performance metrics for assessing the proposed method are the Peak Signal to Noise Ratio (PSNR) and the encoding and decoding time. The results show that the proposed method has a high embedding capacity and efficiency, with a PSNR ranging between 64 and 75 dBs, which indicates that the embedded data is almost indistinguishable from the original video. Additionally, the method can encode and decode data quickly, making it efficient for real time applications.

Problem

• Optimizing ROI selection using genetic algorithms for video steganography
• Ensuring data security through AES encryption before embedding
• Maintaining high video quality with PSNR between 64-75 dB

Innovation

• Genetic Algorithm optimizes Region of Interest selection
• AES encryption secures data before embedding
• High PSNR ensures minimal video quality degradation