Detecting covert information in images by means of steganalysis techniques has become increasingly necessary due to the amount of data being transmitted mainly through the Internet. However, these techniques are computationally expensive and not much attention has been paid to reduce their cost by means of available parallel computational platforms. This article presents two computational models for the Subtractive Pixel Adjacency Model (SPAM) which has shown the best detection rates among several assessed steganalysis techniques. A hardware architecture tailored for reconfigurable fabrics is presented achieving high performance and fulfilling hard real-time constraints. On the other hand, a parallel computational model for the CUDA architecture is also proposed. This model presents high performance during the first stage but it faces a bottleneck during the second stage of the SPAM process. Both computational models are analyzed in detail in terms of their algorithmic structure and performance results. To the best of Authors’ knowledge these are the first design proposals to accelerate the SPAM model calculation. 
An analysis of computational models for accelerating the subtractive pixel adjacency model computation