Deep Industrial Espionage

TL;DR

This paper introduces Deep Industrial Espionage, a neural network framework designed to reverse-engineer, rebrand, and distribute product copies efficiently from a single image, aiming to provide tangible business advantages.

Contribution

It presents a novel end-to-end neural network approach for industrial information propagation and product imitation, expanding beyond traditional object recognition tasks.

Findings

Efficient single-pass product copying framework

Potential for rapid market entry with counterfeit products

Business value in industrial espionage applications

Abstract

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.