Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to their highly parallelizable computation and flexibility in modeling correlation in sequence, to tackle steganalysis problem of Quantization Index Modulation (QIM) based steganography in compressed VoIP stream. We design a light-weight neural network named Fast Correlation Extract Model (FCEM) only based on a variant of attention called multi-head attention to extract correlation features from VoIP frames. Despite its simple form, FCEM outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) models on both prediction accuracy and time efficiency. It significantly improves the best result in detecting both low embedded rates and short samples recently. Besides, the proposed model accelerates the detection speed as twice as before when the sample length is as short as 0.1s, making it a excellent method for online services.

With the growing attention on multimedia security and rapid development of natural language processing technologies, various linguistic steganographic algorithms based on automatic text generation technology have been proposed increasingly, which brings great challenges in maintaining security of cyberspace. The prevailing linguistic steganalysis methods based on neural networks only conduct linguistic steganalysis with feature vectors from last layer of neural network, which may be insufficient for neural linguistic steganalysis. In this paper, we propose a neural linguistic steganalysis scheme based on densely connected Long short-term memory networks (LSTM) with feature pyramids which can incorporate more low level features to detect generative text steganographic algorithms. In the proposed framework, words in text are firstly mapped into semantic space with a hidden representation for better exploitation of the semantic features. Then, stacked bidirectional Long short-term memory networks are ultilized to extract different levels of semantic features. In order to incorporate more low level features from neural networks, we introduced two components: dense connections and feature pyramids to enhance the low level features in feature vectors. Finally, the semantic features from all levels are fused and we use a sigmoid layer to categorize the input text as cover or stego. Experiments showed that the proposed scheme can achieve the state-of-the-art results in detecting recently proposed linguistic steganographic algorithms.

As one of the largest B2C e-commerce platforms in China, JD.com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images. This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms. We also build a highly efficient infrastructure to accomplish end-to-end training with CNN on the 10 billion scale real production dataset within 24 hours, and meet the low latency requirements of online system (20ms on CPU). CSCNN is now deployed in the search advertising system of JD, serving the main traffic of hundreds of millions of active users.

in this letter, we present a novel and extremely fast steganalysis method for voice over ip (voip) streams, driven by the need for a quick and accurate detection of possible steganography in VoIP streams. We firstly analyzed the correlations in carriers. To better exploit the correlations in code-words, we mapped vector quantization code-words into a semantic space. In order to achieve high detection efficiency, only one hidden layer was utilized to extract the correlations between these code-words. Finally, based on the extracted correlation features, we used the softmax classifier to categorize the input stream carriers. To boost the performance of this proposed model, we incorporate a simple knowledge distillation framework into the training process. Experimental results show that the proposed method achieves state-of-the-art performance both in detection accuracy and efficiency. In particular, the processing time of this method on average is only about 0.05% when sample length is as short as 0.1 s, attaching strong practical value to online serving of steganography monitor.

Steganalysis of the Quantization Index Modulation (QIM) steganography in VoIP (Voice-over IP) stream is conducted in this research. VoIP is a popular media streaming and communication service on the Internet. QIM steganography makes it possible to hide secret information in VoIP streams. Detecting short and low embedding rates of QIM steganography samples remains an unsolved challenge. Recently, neural network models have been demonstrated to be capable of achieving remarkable performances and be successfully applied to many different tasks. The mainstream architectures of neural network include Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which adopt totally different ways to understand various signals. In this paper, we first indicate a proper way to combine the strengths of these two architectures and then construct a novel and unified model called CNN-LSTM network to detect QIM-based steganography. In our model, Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is utilized to capture long time contextual information in carriers and CNN was used subsequently to capture both local features and global ones as well as temporal carrier features. Experiments showed that our model can achieve the state-of-art result in detecting QIM-based steganography in VoIP streams.