Further reading

The following resources include a great deal of information:

• Explaining and Harnessing Adversarial Samples: https://arxiv.org/pdf/1412.6572.pdf
• Delving Into Transferable Adversarial Examples and Black Box Attacks: https://arxiv.org/pdf/1611.02770.pdf
• Foolbox - a Python toolbox to benchmark the robustness of machine learning models: https://arxiv.org/pdf/1707.04131.pdf
• The Foolbox GitHub: https://github.com/bethgelab/foolbox
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN: https://arxiv.org/pdf/1702.05983.pdf
• Malware Images: Visualization and Automatic Classification: https://arxiv.org/pdf/1702.05983.pdf
• SARVAM: Search And RetrieVAl of Malware: http://vision.ece.ucsb.edu/sites/vision.ece.ucsb.edu/files/publications/2013_sarvam_ngmad_0.pdf
• SigMal: A Static Signal Processing Based Malware Triage: http://vision.ece.ucsb.edu/publications...