Steganographic methods attempt to insert data in multimedia signals in an undetectable fashion. However, these methods often disrupt the underlying signal characteristics, thereby allowing detection under careful steganalysis. Under repeated embedding, disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. That is, the marginal distortions due to repeated embeddings decrease monotonically. We name this general principle as the principle of diminishing marginal distortions (DMD) and illustrate its validity in the audio domain using a morphological distortion metric. The principle of DMD is used to derive a steganalysis tool that detects the presence of hidden messages in uncompressed audio files. Detailed analysis and experimental results are provided for the detection of spread spectrum watermarking and stochastic modulation steganography.