Biblio

Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.

In recent years, deep learning has achieved breakthrough results in various areas, such as computer vision, audio recognition, and natural language processing. However, just several related works have been investigated for digital multimedia forensics and steganalysis. In this paper, we design a novel CNN (convolutional neural networks) to detect audio steganography in the time domain. Unlike most existing CNN based methods which try to capture media contents, we carefully design the network layers to suppress audio content and adaptively capture the minor modifications introduced by $\pm$1 LSB based steganography. Besides, we use a mix of convolutional layer and max pooling to perform subsampling to achieve good abstraction and prevent over-fitting. In our experiments, we compared our network with six similar network architectures and two traditional methods using handcrafted features. Extensive experimental results evaluated on 40,000 speech audio clips have shown the effectiveness of the proposed convolutional network.