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We review conventional statistical and machine-learning-based steganalysis approaches, including feature-based detectors (e.g., SPAM, SRM), Rich Models, and modern convolutional neural networks trained end-to-end for detection. Experimental comparisons (summarized from literature) show transform-domain methods generally outperform simple LSB in resisting statistical tests, while adaptive and content-aware schemes further reduce detectability. However, ML-based steganalyzers—especially deep-learning classifiers trained on representative datasets—have substantially narrowed the gap, detecting many previously robust methods when sufficient training data exists.