Steganalysis Model Research Articles

RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network

Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an unsolved challenge. In this paper, we propose an effective online steganalysis method to detect QIM steganography. We find four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. To extract those correlation features, we propose the codeword correlation model, which is based on recurrent neural network (RNN). Furthermore, we propose the feature classification model to classify those correlation features into cover speech and stego speech categories. The whole RNN-based steganalysis model (RNN-SM) is trained in a supervised learning framework. Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0.1 s, and is significantly higher than other state-of-the-art methods. For the challenging task of conducting steganalysis towards low embedding rate samples, RNN-SM also achieves a high accuracy. The average testing time for each sample is below 0.15% of sample length. These clues show that RNN-SM meets the short sample detection demand and is a state-of-the-art algorithm for online VoIP steganalysis.

Deep residual learning for image steganalysis

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.

Adaptive Steganalysis Based on Embedding Probabilities of Pixels

In modern steganography, embedding modifications are highly concentrated on the textural regions within an image, as such regions are difficult to model for steganalysis. Previous studies have shown that compared with non-adaptive strategies, this content adaptive strategy achieves stronger security against existing steganalysis. Based on the experiments and analyses, however, we found that this embedding property would inevitably lead to a large limitation in existing adaptive steganography. That is, it is possible for steganalyzers to estimate the regions that have probably been modified after data hiding. In this paper, we propose an adaptive steganalytic scheme based on embedding probabilities of pixels. The main idea of our scheme is that we assign different weights to different pixels in feature extraction. For those pixels with high embedding probabilities, their corresponding weights are larger, since they should contribute more to steganalysis and vice versa. By doing so, we can concentrate our attention on the regions that have probably been modified and significantly reduce the impact of other unchanged smooth regions. It is expected that our proposed method is an improvement on the existing steganalytic methods, which usually assume every pixel has the same contribution to steganalysis. The extensive experiments evaluated on four typical adaptive steganographic methods have shown the effectiveness of the proposed scheme, especially for low embedding rates, for example, lower than 0.20 bpp.