# Neural network image copyright applications

As neural networks and artificial intelligence expand, companies such as Google (GOOG: Alphabet) can employ neural networks for the purpose detecting copyright infringement in photos used on websites (blogs) and uploaded to the internet.  Neural network image copyright applications will no doubt be an application of artificial intelligence in the intellectual property arena.  Having the ability to scan hundreds of thousands of images a second, an AI neural network can be tasked to look for a certain image. Computer imaging technology can convert a simple picture into a digital matrix (e.g., a “red, green, blue” matrix) of digital values which is fed into the neural network.  From there images across the internet can be searched using Logistic Regression to determine which images likely infringe the copyrighted image.

## How to understand a neural network search for copyrighted images

To understand how a neural network image copyright search would apply to the real world, we can think of the neural network as a dog which is tasked to “sniff” out the smell of a lost child. The child’s personal item (e.g., a jacket belonging to that lost child) is digitized (akin to the digitization of a copyrighted image) and is placed under the nose of the dog (fed into the neural network) so that it can obtain the scent of the child. The dog then proceeds to search for the lost child (just as the neural network searches for images that are identical to [or “substantially similar” to, as copyright law would have it]), thus finding the lost child.

### Using Logistic Regression to Explain How a Neural Network Would Search For a Copyrighted Image

Techy Stuff: To get a bit into the mechanics of how the ocean of images on the internet could be classified as being a “match” or not to the image fed into the neural network, the computer would “score” the probability of whether each image is a match or not. As such, the neural network would employ something called “Logistic Regression,” where it identifies each of the data points of the digitized image as “x”, and it would plot the values on a y = wx + b graph. However, since we are looking for probability values between zero (0) and one (1), we would be looking for the “sigma” of wx + b / z [where “z” is the x-axis, and “sigma” values range from almost 0 (0% match) to almost 1 (100% match), although never touching 0% or 100% because we can never be 100% sure of whether an image is a match or not]. The output of this (sigma ((w x + b) / z)) is called “y hat”.

With artificial intelligence and the deployment of neural networks, an intellectual property right holder (a copyright holder) could easily search for and identify images that are substantially similar to their copyrighted image.

Techy Stuff: With the application of logistic regression, the way a neural network “understands” whether the results it is outputting [based on the algorithm(s) you fed it] are accurate or not. A neural network does this by comparing the “y hat” to the “y” (a digitized version of the original item). This comparison between the “y hat” output and the original is called the “loss function” for that particular output. This is akin to comparing a random object the dog searching for the lost child comes across as matching the scent of the lost child. In the copyright infringement analogy, the loss function is the comparison of the image posted on a website to the copyrighted image).

#### Measuring and adjusting the efficiency of neural networks using loss functions and cost functions.

Cost functions are something slightly different. A cost function measures the overall effectiveness of a search, taking into consideration the outputs of the loss function. Once the cost function (“J”) is obtained (to learn how effective the outputs of a neural network is), a neural network would then begin to employ something called a “gradient descent.” Think of the results of the cost function as an inverted parachute, and on every point of the inverted parachute (except the center at the lowest point), there is a slope. The lowest point is the point where the results of the neural network are most accurate. Thus, the neural network will analyze the slope at every point and it will sidestep either to the right or the left (on the parachute) until it finds itself at the lowest point. This allows the neural network to minimize its cost function and output the most accurate results.

[NOTE: While I am attempting to simplify the idea of the concepts of “logistic regression,” a “loss function” and a “cost function,” the point is that in order to understand what a neural network is, it is important to understand what a neural network does. Thus far, it appears to me as if a neural network employs a logistic regression in order to compare one object to another.]

## How neural network image copyright applications can and will be misused

While the difficulties which arise as the copyright holder then retains an attorney to seek significant monetary damages are a direct result and will be a significant issue to contend with in coming years and decades, the topic of monetary enforcement of the copyright rights are outside the scope of this article.