Abstract:

Steganography plays a crucial role in securing sensitive information by concealing text within digital images. This research proposes a novel text-based image steganography method that integrates histogram equalization and CAPTCHA authentication to enhance both security and imperceptibility. Histogram equalization is employed as a preprocessing step to enhance contrast and redistribute pixel intensities, ensuring a more uniform embedding space while reducing susceptibility to statistical detection. Instead of traditional Least Significant Bit (LSB) substitution, a more robust frequency-domain or pixel-intensity modulation approach is utilized to embed textual data, minimizing distortion while maintaining the integrity of the cover image. CAPTCHA authentication is implemented to prevent automated attacks and restrict unauthorized access to the hidden text.

Introduction:

In an era of heightened cybersecurity threats, ensuring secure data transmission has become imperative. Image steganography provides an effective solution by embedding confidential information within images, thereby concealing its presence from unauthorized users. However, conventional methods such as Least Significant Bit (LSB) substitution are vulnerable to statistical and visual steganalysis attacks, making them less secure for critical applications. To enhance robustness and improve resistance to detection, this study explores an alternative approach that integrates histogram equalization as a preprocessing step. This technique redistributes pixel intensity values, increasing embedding capacity while maintaining imperceptibility.
To further enhance security, CAPTCHA authentication is incorporated as an access control mechanism to prevent automated extraction by malicious entities. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) ensures that only legitimate users can retrieve the embedded text, mitigating the risk of brute-force attacks. By combining advanced steganographic embedding with CAPTCHA-based authentication, the proposed system enhances both data concealment and access security. This approach is particularly suitable for applications requiring confidential communication, secure watermarking, and digital forensics, ensuring that hidden information remains protected against unauthorized retrieval and detection.
Furthermore, the integration of histogram equalization in the steganographic process enhances the security and effectiveness of data hiding. Histogram equalization improves the contrast of an image by redistributing its pixel intensity values, making it more resistant to steganalysis techniques that rely on statistical irregularities. By equalizing the histogram before embedding text, the method ensures that the hidden data remains imperceptible while maximizing the image's capacity to hold information. This approach mitigates the risks associated with traditional techniques, as it prevents noticeable distortions that could reveal the presence of hidden data.
Additionally, the use of CAPTCHA authentication strengthens the security framework by introducing a human-verification layer before accessing the concealed text. Conventional steganographic methods often lack robust access control, making them susceptible to unauthorized retrieval through automated attacks. By requiring users to solve a CAPTCHA before extracting the hidden information, the system effectively blocks bots and malicious actors from gaining access. This mechanism is particularly useful in secure communication platforms where sensitive information must be protected from automated decryption attempts.

Moreover, this technique finds extensive applications in secure digital communications, watermarking, and forensic analysis. In environments where confidentiality is paramount, such as military communications, medical records protection, and copyright enforcement, the combination of histogram equalization-based steganography and CAPTCHA authentication ensures that hidden data remains undetectable and accessible only to authorized users. Future enhancements could involve deep learning-based CAPTCHA mechanisms and adaptive embedding techniques to further improve security and scalability. By integrating multiple layers of protection, this approach paves the way for more robust, secure, and intelligent steganographic systems.

Objective:

Hiding text in image steganography using CAPTCHA authentication