标题： 基于真实世界演员的图像隐写分析通过分类器不一致检测 显示英文标题

标题： Real-world actor-based image steganalysis via classifier inconsistency detection

摘要： 在本文中，我们提出了一种稳健的方法，用于在图像隐写术中检测有罪行为者，同时有效解决覆盖源不匹配（CSM）问题，该问题发生在使用从另一个源训练的分类器对来自一个源的图像进行分类时。 设计用于基于行为者的场景，我们的方法结合了分类器不一致检测（DCI）预测与EfficientNet神经网络进行特征提取，并使用梯度提升机进行最终分类。 所提出的方法成功确定行为者是否无辜或有罪，或者由于过多的CSM是否应被丢弃。 我们表明，即使在高CSM的场景中，该方法仍然可靠，持续达到80%以上的准确率，并优于基线方法。 这种新方法通过提供处理CSM和检测现实应用中有罪行为者的一种实用且高效解决方案，为隐写分析领域做出了贡献。 显示英文摘要

摘要： 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.