ImageCompressionAndEncryption-master: Hybrid Compression and Encryption Technique Using Circulant Matrix Control

In today's multimedia and technology-driven world, image security has emerged as a critical priority in information management. The value of an image is inherently tied to its informational content, and unauthorized access to such data can lead to severe consequences. To safeguard images, numerous encryption techniques have been developed, with compressive sensing gaining significant attention in recent years. Compressive sensing is an innovative technique that performs simultaneous sampling and compression. When integrated with encryption methods, it significantly enhances image security. Research demonstrates that compressive sensing-based encryption achieves computational security and robustness through its intrinsic multidimensional projection perturbation feature, which effectively thwarts privacy breaches. The implementation typically involves constructing a measurement matrix—often using algorithms like Gaussian random matrices or Bernoulli matrices—to capture sparse representations of images while encrypting them. However, conventional compressive encryption algorithms utilize the entire measurement matrix as the encryption key, resulting in excessively large keys that are impractical for allocation, distribution, and memorization. Additionally, earlier schemes struggled to perform compression and encryption concurrently, leading to operational inefficiencies. To address these limitations, a hybrid compression technique was developed. This approach employs key-controlled circulant matrices for the measurement matrix construction, significantly reducing key size through matrix periodicity properties. The original image is segmented into four blocks using region division algorithms, each undergoing parallel compression and encryption. These processed blocks are then scrambled via random pixel exchange with dynamically generated random matrices—implemented through permutation functions like Fisher-Yates shuffling—ensuring robust security against privacy intrusions. In summary, the integration of compressive sensing with complementary encryption methods proves highly effective for image security enhancement. The hybrid technique successfully resolves key scalability and operational efficiency issues inherent in traditional compressive-based encryption, establishing a more secure and practical framework for image encryption.