The paper proposes an alternative to support vector machines (SVMs) for steganalysis, using ensemble classifiers implemented as random forests. The authors argue that ensemble classifiers are well-suited for steganalysis due to their scalability with the number of training examples and feature dimensionality, while maintaining comparable performance to SVMs. The ensemble classifier is described in detail, including the use of bootstrap samples and random feature subspaces to increase diversity among base learners. The paper also discusses the parameter determination process for optimal performance and compares the ensemble classifier's complexity and performance with SVMs. Experimental results demonstrate the effectiveness of the proposed framework on three steganographic methods: nsF5, YASS, and MBS, showing improved detection accuracy compared to SVMs. The ensemble classifier is particularly advantageous for handling high-dimensional feature spaces and large training sets, making it a powerful tool for steganalysis.The paper proposes an alternative to support vector machines (SVMs) for steganalysis, using ensemble classifiers implemented as random forests. The authors argue that ensemble classifiers are well-suited for steganalysis due to their scalability with the number of training examples and feature dimensionality, while maintaining comparable performance to SVMs. The ensemble classifier is described in detail, including the use of bootstrap samples and random feature subspaces to increase diversity among base learners. The paper also discusses the parameter determination process for optimal performance and compares the ensemble classifier's complexity and performance with SVMs. Experimental results demonstrate the effectiveness of the proposed framework on three steganographic methods: nsF5, YASS, and MBS, showing improved detection accuracy compared to SVMs. The ensemble classifier is particularly advantageous for handling high-dimensional feature spaces and large training sets, making it a powerful tool for steganalysis.