Mode: Loss Function for Deep Steganalysis at Low False Positive Rate

Deep steganalysis has been crucial in detecting hidden messages in digital media for nearly a decade. However, its common security evaluation criterion-the probability of error under equal prior-fails to reflect real forensic challenges. In practice, low False Positive (FP) rates matter most but are only adjusted empirically post-training. Standard classifiers, trained with cross-entropy loss, optimize balanced error rates rather than minimizing FPs. We propose a framework that integrates the likelihood ratio test into the loss function to optimize deep classifiers for low FP rates. Our method outperforms standard cross-entropy and other modern approaches, as demonstrated on the BOSSBase dataset across FP rates of 10⁻³ to 10⁻¹ in both uncompressed and JPEG domains.