Comprehensive Comparison of Image Steganography Techniques with Security Enhancement
Hessah Alshamrani¹, Samah Alajmani², Raneem Yousif Alyami³, Ben Soh⁴

¹Hessah Alshamrani, Department of Cyber Security, Taif University, Taif, Saudi Arabia.

²Dr. Samah Hazzaa Alajmani, Assistant Professor, Department of Information Technology, Taif University, Taif, Saudi Arabia.

³Dr. Raneem Yousif Alyami, Assistant Professor, Department of Information Technology, Taif University, Taif, Saudi Arabia.

⁴Dr. Ben Soh, Associate Professor, Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.        

Manuscript received on 01 May 2025 | First Revised Manuscript received on 09 May 2025 | Second Revised Manuscript received on 17 June 2025 | Manuscript Accepted on 15 July 2025 | Manuscript published on 30 July 2025 | PP: 7-19 | Volume-14 Issue-2, July 2025 | Retrieval Number: 100.1/ijrte.A824914010525 | DOI: 10.35940/ijrte.A8249.14020725

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents an extensive comparative analysis of several image steganography methods, examining their efficiency in terms of capacity, media quality, resistance to detection, and computational efficiency. Steganography, the science of hiding sensitive data within a larger image, is vital for secure data transmission. The research compares five of the most used techniques: Least Significant Bit (LSB), RGB, Pixel Value Differencing (PVD), Feature-Based Optimised Steganography, and the VisionStego AI System. The introduction emphasises the importance of cryptography and steganography in concealing data. Cryptography encrypts the data, whereas steganography hides data within multimedia content. Image steganography conceals information by distorting cover images in a way that makes the information difficult to retrieve. Steganalysis is crucial in the retrieval of concealed information. LSB, RGB, and PVD are traditional steganography methods that suffer from drawbacks such as low capacity, media degradation, and detectability. This study will contrast and compare the performance of LSB, RGB, PVD, Feature-Based Optimized approaches, and the VisionStego AI System. A literature review explains various methods in steganography and their evolution. Scientists have discovered that combining encryption and steganography provides enhanced data security. Advanced techniques, such as CNN-based and GAN-based methods, have replaced traditional approaches. Hashing techniques and robust linked list steganography have been proposed to offer greater protection. Generative adversarial networks (GANs) and invertible steganography networks (ISNs) have emerged as methods to enhance capacity, security, and robustness. The methodology section compares five steganography techniques: LSB, RGB, PVD, Feature-Based Optimized Steganography, and VisionStego AI System. The performance of each method is based on capacity, media quality, detection resistance, and computational complexity. The comparison metrics are Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), embedding time, and extraction time. The LSB technique substitutes the least significant bit of the cover image with secret information. It is simple and fast, but has low capacity and is detectable. The test results indicate that LSB offers high-speed embedding and good quality, but has low capacity and security. The RGB technique disperses the secret data in the red, green, and blue components of the cover image. It offers better data hiding quality and fewer visual distortions compared to LSB, but with a reduced extraction rate; hence, it is not suitable for applications that require rapid data retrieval. The PVD technique conceals information by looking for pixel value differences in the cover image. It is less detectable to manipulation and reduces visual noise. But it is more complicated to operate and can be identified when image enhancement techniques are applied. Feature-Based Optimized Steganography uses information embedding based on some features of multimedia, i.e., image corners and edges. It incorporates data encryption and compression before information embedding, lowering PSNR, SSIM, and MSE. The scheme offers satisfactory security and minimum storage space, but increases extraction and embedding times due to compression and encryption. The VisionStego AI System utilises artificial intelligence to automatically calculate effective places within the image where data must be embedded. It embeds text data in a digital format and calculates areas of importance in the image through saliency detection. It offers good security, optimised storage size, does not distort the image, and provides simple retrieval, along with outstanding security. The outcomes of the performance test reveal that the VisionStego AI System achieves a satisfactory compromise between data concealment quality, storage efficiency, and processing efficiency. The discussion and results section demonstrate that the VisionStego AI System outperforms other systems in preserving image quality after embedding. It achieves high PSNR and SSIM, which are metrics for zero and imperceptible distortion. Feature-Based Steganography maximizes the spreading of data, thereby enhancing concealment performance with no impact on visual coherence. Low measures of MSE confirm that the embedding procedure ensures a subtle and almost imperceptible modification of the image. The VisionStego AI System can offer hiding capacity preservation while maintaining image quality, providing large-scale data embedding in a lossless, imperceptible form. VisionStego AI is furthermore less vulnerable to steganalysis attacks compared to conventional LSB-based methods. With the addition of encryption and compression processes, this further enhances its security. The conclusion and future work focus on how the projected VisionStego AI System exhaustively surpasses LSB-based techniques in terms of concealment capability and confidentiality, while retaining higher image fidelity and resilience against steganalysis attacks. The system’s ability to perform optimal data embedding without distorting the structural integrity of the image is evident from the high values of PSNR and SSIM. Future research could focus on optimising the embedding process according to other image characteristics, conducting additional experiments on different types of images, and enhancing detection resistance and extraction resistance using higher-level encryption and obfuscation techniques.

Keywords: Image Steganography, Least Significant Bit, Pixel Value Differencing, Feature-based Optimise & VisionStego AI.
Scope of the Article: Information Technology