Biblio

The paper proposes a new technique to detect Android malware effectively based on converting malware binaries into images and applying machine learning techniques on those images. Existing research converts the whole executable files (e.g., DEX files in Android application package) of target apps into images and uses them for machine learning. However, the entire DEX file (consisting of header section, identifier section, data section, optional link data area, etc.) might contain noisy information for malware detection. In this paper, we convert only data sections of DEX files into grayscale images and apply machine learning on the images with Convolutional Neural Networks (CNN). By using only the data sections for 5,377 malicious and 6,249 benign apps, our technique reduces the storage capacity by 17.5% on average compared to using the whole DEX files. We apply two CNN models, Inception-v3 and Inception-ResNet-v2, which are known to be efficient in image processing, and examine the effectiveness of our technique in terms of accuracy. Experiment results show that the proposed technique achieves better accuracy with smaller storage capacity than the approach using the whole DEX files. Inception-ResNet-v2 with the stochastic gradient descent (SGD) optimization algorithm reaches 98.02% accuracy.

In this paper, inspired by Gatys's recent work, we propose a novel approach that transforms photos to comics using deep convolutional neural networks (CNNs). While Gatys's method that uses a pre-trained VGG network generally works well for transferring artistic styles such as painting from a style image to a content image, for more minimalist styles such as comics, the method often fails to produce satisfactory results. To address this, we further introduce a dedicated comic style CNN, which is trained for classifying comic images and photos. This new network is effective in capturing various comic styles and thus helps to produce better comic stylization results. Even with a grayscale style image, Gatys's method can still produce colored output, which is not desirable for comics. We develop a modified optimization framework such that a grayscale image is guaranteed to be synthesized. To avoid converging to poor local minima, we further initialize the output image using grayscale version of the content image. Various examples show that our method synthesizes better comic images than the state-of-the-art method.