  The theory of deep learning is now considered largely solved, and is well
understood by researchers and influencers alike. To maintain our relevance, we
therefore seek to apply our skills to under-explored, lucrative applications of
this technology. To this end, we propose and Deep Industrial Espionage, an
efficient end-to-end framework for industrial information propagation and
productisation. Specifically, given a single image of a product or service, we
aim to reverse-engineer, rebrand and distribute a copycat of the product at a
profitable price-point to consumers in an emerging market---all within in a
single forward pass of a Neural Network. Differently from prior work in machine
perception which has been restricted to classifying, detecting and reasoning
about object instances, our method offers tangible business value in a wide
range of corporate settings. Our approach draws heavily on a promising recent
arxiv paper until its original authors' names can no longer be read (we use
felt tip pen). We then rephrase the anonymised paper, add the word "novel" to
the title, and submit it a prestigious, closed-access espionage journal who
assure us that someday, we will be entitled to some fraction of their
extortionate readership fees.
