Ensemble classifiers, specifically random forests, are proposed as an alternative to support vector machines (SVMs) for steganalysis of digital media. The paper argues that ensemble classifiers are more scalable and efficient, allowing for the use of high-dimensional feature sets and larger training data, which are essential for detecting modern steganographic algorithms. Ensemble classifiers are shown to provide improved detection accuracy across various embedding methods. The paper demonstrates the effectiveness of ensemble classifiers on three steganographic methods that hide messages in JPEG images. The paper discusses the challenges of steganalysis, including the difficulty of modeling empirical covers and the need for accurate detection of embedding changes. It highlights the limitations of SVMs, such as high training complexity and sensitivity to feature dimensionality. Ensemble classifiers, on the other hand, offer more flexibility and can handle larger feature spaces and training sets, leading to faster development cycles and better performance. The paper introduces an ensemble classifier based on random forests, where each base learner is trained on a random subspace of the feature space. The final decision is made by aggregating the decisions of individual base learners. The paper also describes the process of determining the optimal parameters for the ensemble classifier, including the subspace dimension and the number of base learners. The paper provides an illustrative example using the nsF5 steganographic algorithm, demonstrating the effectiveness of the ensemble classifier in detecting hidden messages. It also compares the performance of the ensemble classifier with SVMs, showing that the ensemble classifier achieves comparable or better results with lower computational complexity. The paper concludes that ensemble classifiers, particularly random forests, offer a powerful and scalable solution for steganalysis, enabling the use of complex feature sets and larger training data to improve detection accuracy. The proposed framework is shown to be effective in detecting various steganographic methods, including nsF5, YASS, and MBS.Ensemble classifiers, specifically random forests, are proposed as an alternative to support vector machines (SVMs) for steganalysis of digital media. The paper argues that ensemble classifiers are more scalable and efficient, allowing for the use of high-dimensional feature sets and larger training data, which are essential for detecting modern steganographic algorithms. Ensemble classifiers are shown to provide improved detection accuracy across various embedding methods. The paper demonstrates the effectiveness of ensemble classifiers on three steganographic methods that hide messages in JPEG images. The paper discusses the challenges of steganalysis, including the difficulty of modeling empirical covers and the need for accurate detection of embedding changes. It highlights the limitations of SVMs, such as high training complexity and sensitivity to feature dimensionality. Ensemble classifiers, on the other hand, offer more flexibility and can handle larger feature spaces and training sets, leading to faster development cycles and better performance. The paper introduces an ensemble classifier based on random forests, where each base learner is trained on a random subspace of the feature space. The final decision is made by aggregating the decisions of individual base learners. The paper also describes the process of determining the optimal parameters for the ensemble classifier, including the subspace dimension and the number of base learners. The paper provides an illustrative example using the nsF5 steganographic algorithm, demonstrating the effectiveness of the ensemble classifier in detecting hidden messages. It also compares the performance of the ensemble classifier with SVMs, showing that the ensemble classifier achieves comparable or better results with lower computational complexity. The paper concludes that ensemble classifiers, particularly random forests, offer a powerful and scalable solution for steganalysis, enabling the use of complex feature sets and larger training data to improve detection accuracy. The proposed framework is shown to be effective in detecting various steganographic methods, including nsF5, YASS, and MBS.