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<p>The proposed research introduces a novel steganalytic tactic termed the Imbalanced Maximizing-AUC Proximal Support Vector Machine (PSVM). This method strengthens detection performance in the presence of imbalanced datasets by integrating AUC maximization into the PSVM framework. In doing so, it directly addresses one of the major challenges in steganalysis—class imbalance—while reducing the reliance on extensive hyperparameter tuning, thereby improving model performance when imbalance exists. Theoretically, the approach retains the key advantages of PSVMs, including fast incremental updates, making it well-suited for scenarios requiring rapid and adaptive adjustments. In parallel, an alternative version of the Differential Evolution (DE) scheme is introduced, featuring a novel mutation scheme based on k-means clustering to ensure effective hyperparameter optimization. This mechanism provides resilience and adaptability across diverse conditions. Empirical evaluation on standard databases—BossBase 1.01 and BOWS-2—reveals substantial improvements, achieving F-measure scores of 89.86% and 91.55%, respectively, surpassing existing steganalysis methods. Overall, the proposed approach marks a significant advancement in addressing class imbalance and optimizing detection efficiency, establishing a strong benchmark for future research in image steganalysis.</p>

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Imbalanced learning using the area under the curve and proximal support vector machine for image steganalysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月4日

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  • Xinpan LI

摘要

The proposed research introduces a novel steganalytic tactic termed the Imbalanced Maximizing-AUC Proximal Support Vector Machine (PSVM). This method strengthens detection performance in the presence of imbalanced datasets by integrating AUC maximization into the PSVM framework. In doing so, it directly addresses one of the major challenges in steganalysis—class imbalance—while reducing the reliance on extensive hyperparameter tuning, thereby improving model performance when imbalance exists. Theoretically, the approach retains the key advantages of PSVMs, including fast incremental updates, making it well-suited for scenarios requiring rapid and adaptive adjustments. In parallel, an alternative version of the Differential Evolution (DE) scheme is introduced, featuring a novel mutation scheme based on k-means clustering to ensure effective hyperparameter optimization. This mechanism provides resilience and adaptability across diverse conditions. Empirical evaluation on standard databases—BossBase 1.01 and BOWS-2—reveals substantial improvements, achieving F-measure scores of 89.86% and 91.55%, respectively, surpassing existing steganalysis methods. Overall, the proposed approach marks a significant advancement in addressing class imbalance and optimizing detection efficiency, establishing a strong benchmark for future research in image steganalysis.