Denoising and error correction in noisy AES-encrypted images using statistical measures

• This paper addresses the problem of removing bit errors in visual data which are encrypted using the AES algorithm in CBC mode.
• Three statistical measures are proposed, i.e. the global variance method (GVM), the mean local variance method (MLVM) and the sum of the squared derivative method (SSDM) for error correction.
• The proposed approach uses local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove errors.
• Experimental results show that the proposed approach gives better results in removing noise and can be used for noise removal in visual data in the encrypted domain.

Cryptography based techniques are used to secure confidential data from unauthorized access. These techniques are very good for the security and protection of the data but are very sensitive to noise. A single bit change in encrypted data can have a catastrophic impact on the decrypted data. This paper addresses the problem of removing bit errors in visual data which are encrypted using the AES algorithm in CBC mode (Cipher Block Chaining). We propose a noise removal approach based on the statistical analysis of each block during the decryption process. Three statistical measures are proposed, i.e. the global variance method (GVM), the mean local variance method (MLVM) and the sum of the squared derivative method (SSDM) for error correction. The proposed approach uses local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove errors. Experimental results show that the proposed approach gives better results in removing noise and can be used for noise removal in visual data in the encrypted domain.