In this paper, we propose a robust method for detecting guilty actors in image steganography while effectively addressing the Cover Source Mismatch (CSM) problem, which arises when classifying images from one source using a classifier trained on images from another source. Designed for an actor-based scenario, our method combines the use of Detection of Classifier Inconsistencies (DCI) prediction with EfficientNet neural networks for feature extraction, and a Gradient Boosting Machine for the final classification. The proposed approach successfully determines whether an actor is innocent or guilty, or if they should be discarded due to excessive CSM. We show that the method remains reliable even in scenarios with high CSM, consistently achieving accuracy above 80% and outperforming the baseline method. This novel approach contributes to the field of steganalysis by offering a practical and efficient solution for handling CSM and detecting guilty actors in real-world applications.

翻译：本文提出了一种鲁棒的方法，用于检测图像隐写中的有罪参与者，同时有效解决了覆盖源失配问题——该问题在使用来自一个源图像训练的模型对另一源图像进行分类时产生。该方法专为基于参与者的场景设计，结合了分类器不一致性检测预测、用于特征提取的EfficientNet神经网络以及用于最终分类的梯度提升机。所提出的方法能够成功判定参与者是无辜还是有罪，或因过高的CSM而被排除。我们证明，即使在CSM较高的场景中，该方法仍保持可靠，准确率始终高于80%，且优于基线方法。这一新颖方法通过为实际应用中处理CSM和检测有罪参与者提供实用高效的解决方案，为隐写分析领域做出了贡献。