Improved LSB stegananalysis based on analysis of adjacent pixel pairs

We propose a simple, reliable method based on probability of transitions and distribution of adjacent pixel pairs for steganalysis on digital images in spatial domain subjected to Least Significant Bit replacement steganography. Our method is sensitive to the statistics of underlying cover image and is a variant of Sample Pair Method. We use the new method to estimate length of hidden message reliably. The novelty of our method is that it detects from the statistics of the underlying image, which is invariant with embedding, whether the results it calculate are reliable or not. To our knowledge, no steganalytic method so far predicts from the properties of the stego image, whether its results are accurate or not

Undetectable least significant bit replacement steganography

In this paper we propose a novel method based on Inverse Transitions for increasing the security of Least Significant Bit (LSB) replacement steganography. Before hiding data using LSB replacement, cover image is preprocessed using inverse transitions. The preprocessing modifies the LSBs in such a way that the resulting change in pixel values can not occur with LSB replacement. The proposed method ensures 100% undetectability for payload up to 1.5 bpp in colour images against most accurate length estimation methods for LSB replacement. The proposed method is faster, does not require any additional storage and ensures complete recovery of hidden data in comparison to state of the art steganography methods. The proposed method can be used in resource constrained applications which demand fast and secure data hiding and loss less recovery of hidden data

Efficient pattern synthesis for nearest neighbour classifier

Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than simple classification. In this paper, the authors propose a new strategy to reduce the time and memory requirements by applying prototyping as an intermediate step in the synthetic pattern generation technique. Results show that through the proposed strategy, classification can be done much faster without compromising much in terms of classification accuracy, in fact for some cases it gives better accuracy in lesser time. The classification time and accuracy can be balanced according to available memory and computing power of a system to get the best possible results