Machine learning has been a rising technique in signatureless malware detection and is popular in the anti-virus industry. Despite the powerful ability of machine learning, it is known to be vulnerable to attack by injecting specially crafted input noise (adversarial example). In this paper, we develop a systematic attack method that is effective, general and also efficient which automatically generates functional malware. Experiment results showed that such adversarial malware could deceive commercial anti-virus and completely defeat learning-based malware detector provided by a well-known anti-virus vendor. We further examine the effectiveness of our approach on multiple anti-virus engines on VirusTotal and investigate the transferability of our proposed method between different features and classification algorithms. Finally, we show how our attack could resist JavaScript de-obfuscation techniques.

Most of the existing works on image description focus on generating expressive descriptions. The only few works that are dedicated to generating stylish (e.g., romantic, lyric, etc.) descriptions suffer from limited style variation and content digression. To address these limitations, we propose a controllable stylish image description generation model. It can learn to generate stylish image descriptions that are more related to image content and can be trained with the arbitrary monolingual corpus without collecting new paired image and stylish descriptions. Moreover, it enables users to generate various stylish descriptions by plugging in style-specific parameters to include new styles into the existing model. We achieve this capability via a novel layer normalization layer design, which we will refer to as the Domain Layer Norm (DLN). Extensive experimental validation and user study on various stylish image description generation tasks are conducted to show the competitive advantages of the proposed model.