Lina Wang — Page 3 | Searcharxiv

arXiv2019

Spec-ResNet: A General Audio Steganalysis scheme based on Deep Residual Network of Spectrogram

The widespread application of audio and video communication technology make the compressed audio data flowing over the Internet, and make it become an important carrier for covert communication. There are many steganographic schemes emerged in the mainstream audio compression data, such as AAC and MP3, followed by many steganalysis schemes. However, these steganalysis schemes are only effective in the specific embedded domain. In this paper, a general steganalysis scheme Spec-ResNet (Deep Residual Network of Spectrogram) is proposed to detect the steganography schemes of different embedding domain for AAC and MP3. The basic idea is that the steganographic modification of different embedding domain will all introduce the change of the decoded audio signal. In this paper, the spectrogram, which is the visual representation of the spectrum of frequencies of audio signal, is adopted as the input of the feature network to extract the universal features introduced by steganography schemes; Deep Neural Network Spec-ResNet is well-designed to represent the steganalysis feature; and the features extracted from different spectrogram windows are combined to fully capture the steganalysis features. The experiment results show that the proposed scheme has good detection accuracy and generality. The proposed scheme has better detection accuracy for three different AAC steganographic schemes and MP3Stego than the state-of-arts steganalysis schemes which are based on traditional hand-crafted or CNN-based feature. To the best of our knowledge, the audio steganalysis scheme based on the spectrogram and deep residual network is first proposed in this paper. The method proposed in this paper can be extended to the audio steganalysis of other codec or audio forensics.

cs.MM↗

arXiv2017

A Procedural Texture Generation Framework Based on Semantic Descriptions

Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as "regular," "lacelike," and "repetitive" and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.

cs.CV↗