Deep learning architectures have been used with great success to mimic or exceed human visual perception in well-scoped tasks ranging from identifying cats in Youtube videos to cars in self driving systems. Rarely have these techniques been applied to information security. Attacks that attempt to exploit visual perception, such as phishing documents that persuade humans to enable malicious macros and URL (e.g., www.rnicrosoft.com) and file-based (e.g., chr0me.exe) homoglyph attacks, are ripe for similar automated analysis. Our research introduces two methods – SpeedGrapher and Blazar – for leveraging artificial vision systems and features generated by image creation to detect phishing. SpeedGrapher analyzes the appearance of Microsoft (MS) Word documents and leverages an object detection network to identify relevant visual cues to classify samples. Blazar analyzes strings for possible domain or filename spoofing and uses a siamese convolutional neural network and a nearest neighbor index to compare visual similarity of spoofs to known domain or file names with a much greater accuracy than edit distance techniques.