Single-image steganalysis in real-world scenarios based on classifier inconsistency detection

Single-image steganalysis in real-world scenarios based on classifier inconsistency detection

연구 분야: Strategies

학회: ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

This paper presents an improved method for estimating the accuracy of a model based on images intended for prediction, enhancing the standard Detection of Classifier Inconsistencies (DCI) method. The conventional DCI method typically requires a large enough set of images from the same source to provide accurate estimations, which limits its practicality. Our enhanced approach overcomes this limitation by generating a set of images from a single original image, thereby enabling the application of the standard DCI method without requiring more than one target image. This method ensures that the generated images maintain the statistical properties of the original, preserving any embedded steganographic messages, through the use of non-destructive image manipulations such as flips, rotations, and shifts. Experimental results demonstrate that our method produces results comparable to those of the traditional DCI method, effectively estimating model accuracy with as few as 32 generated images. The robustness of our approach is also confirmed in challenging scenarios involving cover source mismatch (CSM), making it a viable solution for real-world applications.