Optimization Strategies Applied to Deep Learning Models for Image Steganalysis: Application of Pruning, Quantization and Weight Clustering

Image steganalysis methods aim at detecting whether there exist hidden messages in images. Deep learning (DL) models have been proposed to enhance steganography detection. These models occupy a large amount of memory and, for this reason, should be optimized when the scenario involves resource-limited devices and systems. This work addresses different deep learning model optimization strategies, namely model pruning, quantization and weight clustering, applied to a deep learning model that presents competitive accuracy results in image steganalysis and belongs to the family of DL models with smaller memory requirements. The results show that the use of optimization schemes can lead to similar or even better accuracy compared to the original model (without the use of optimization schemes), while requiring less memory to store the model. Different scenarios are simulated for each optimization technique, and, finally, quantization is combined with pruning. For dynamic range quantization (DRQ), we achieve models that can save approximately 72% of storage. For FP16 quantization, we obtain better accuracy results and a model with approximately 50% less memory consumption. By applying weight clustering, we also achieve compressed models that can save more than 72% of storage space and lead to better accuracy for some scenarios. Using the combination of pruning and quantization, smaller models in terms of memory requirements are obtained.