Maximum Error Insertion (MEI): A Novel Benchmarking Method for Data Hiding Algorithms

Abstract

Data hiding is becoming increasingly important due to the growing threat to the privacy and security of data from intruders and hackers. This situation is accompanied by the advancement in artificial intelligence applications designed to reveal hidden data, making it difficult to choose the most appropriate hiding approach from those presented in literature. The benchmarking method serves as an important roadmap for making decisions. We propose a distinct plain benchmarking method called maximum error insertion (MEI) benchmarking. This approach intends to hide data using maximum error insertion. The MEI refers to the maximum amount of distortion that can be added to host data (such as image or audio) while still ensuring the successful retrieval of hidden data. The maximum error that can be generated by each hiding algorithm is intentionally inserted to the media file, thus giving us maximum error, maximum capacity, and maximum sensitivity to signal processing attacks. Investigation of the two hiding algorithms demonstrates their applicability and precision, and their implementation significantly enhances the reliability of results during the benchmarking stage.