Image Scrambling Algorithm Based on Bitwise XOR Operations Between Adjacent Pixels

The image scrambling algorithm based on bitwise XOR operations between adjacent pixels effectively modifies the image histogram and demonstrates excellent performance. This algorithm achieves image scrambling by performing XOR operations on image pixels, thereby altering the image characteristics and distribution patterns. Through this approach, the statistical properties of the original image are disrupted, enhancing both the difficulty of analysis and the security of the image. From an implementation perspective, the algorithm typically processes pixels sequentially (either row-wise or column-wise), where each pixel's value is XORed with its adjacent pixel's value. A common implementation approach involves: 1. Loading the image matrix and converting pixel values to appropriate data types 2. Applying XOR operations between consecutive pixels using bitwise operators 3. Handling edge cases where pixels may lack adjacent neighbors 4. Optional iterative applications to increase scrambling intensity Key functions in implementation would include: - Bitwise XOR operations (e.g., using bitxor() in MATLAB or ^ operator in Python) - Pixel value manipulation functions - Image boundary handling mechanisms This image scrambling technique shows promising application prospects in both image processing and information security fields, particularly for digital watermarking, secure transmission, and privacy protection scenarios where image content needs to be obfuscated while preserving reversible transformation capabilities.