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The theory of deep learning is now considered largely solved, and is wellunderstood by researchers and influencers alike. To maintain our relevance, wetherefore seek to apply our skills to under-explored, lucrative applications ofthis technology. To this end, we propose and Deep Industrial Espionage, anefficient end-to-end framework for industrial information propagation andproductisation. Specifically, given a single image of a product or service, weaim to reverse-engineer, rebrand and distribute a copycat of the product at aprofitable price-point to consumers in an emerging market---all within in asingle forward pass of a Neural Network. Differently from prior work in machineperception which has been restricted to classifying, detecting and reasoningabout object instances, our method offers tangible business value in a widerange of corporate settings. Our approach draws heavily on a promising recentarxiv paper until its original authors' names can no longer be read (we usefelt tip pen). We then rephrase the anonymised paper, add the word "novel" tothe title, and submit it a prestigious, closed-access espionage journal whoassure us that someday, we will be entitled to some fraction of theirextortionate readership fees.