Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT - 2015 PROJECT TITLE: Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT - 2015 ABSTRACT: This paper introduces a completely unique feature set for steganalysis of JPEG pictures. The options are built as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-referred to as undecimated DCT). This approach can be interpreted as a projection model in the JPEG domain, forming therefore a counterpart to the projection spatial wealthy model. The most appealing side of this proposed steganalysis feature set is its low computational complexity, lower dimensionality as compared with other rich models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Image Processing Projects A Two-Layer Recurrent Neural Network for Nonsmooth Convex Optimization Problems - 2015 Face Spoof Detection With Image Distortion Analysis - 2015
