Application of Machine Learning and Artificial Intelligence in Adobe.

Raj Kumar Vishwakarma
4 min readNov 19, 2020


Hello Everyone, I am here to discuss about the Application of Artificial Intelligence and Machine Learning in the Adobe.

Lets first understand what machine learning and artificial intelligence is ;

Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly

Artificial Intelligence and the Machine Learning is covering almost every industry to be benefited off.

Now let’s talk about the adobe;

About Adobe:

Adobe Inc. is an American multinational computer software company. Headquartered in San Jose, California, it has historically focused upon the creation of multimedia and creativity software products.

Now let’s discuss the application of machine learining and artificial intelligence in adobe.

Spotting Image Manipulation with AI:

Today, people edit images to achieve new heights of artistOn the otherhand, some people use these powerful tools to “doctor” photos for deceptive purposes. Any technology can be used for both the best and the worst it totally depend on the human intent.

Vlad Morariu, senior research scientist at Adobe, has been working on technologies related to computer vision for many years. Vlad explains that a variety of tools already exist to help document and trace the digital manipulation of photos.

“File formats contain metadata that can be used to store information about how the image was captured and manipulated. Forensic tools can be used to detect manipulation by examining the noise distribution, strong edges, lighting and other pixel values of a photo. Watermarks can be used to establish original creation of an image.”

Every time an image is manipulated, it leaves behind clues that can be studied to understand how it was altered. Each of these techniques tend to leave certain artifacts, such as strong contrast edges, deliberately smoothed areas, or different noise patterns by these it can be understand that how it is manipulated.

Now, what used to take a forensic expert hours to do can be done in seconds. The results of this project are that AI can successfully identify which images have been manipulated. AI can identify the type of manipulation used and highlight the specific area of the photograph that was altered.

An example of authentic images, manipulated images, the RGB and noise streams used to detect manipulation, and the results of AI analysis.

The first technique uses an RGB stream (changes to red, green and blue color values of pixels) to detect tampering. The second uses a noise stream filter. Image noise is random variation of color and brightness in an image and produced by the sensor of a digital camera or as a by product of software manipulation. It looks a little like static. Many photographs and cameras have unique noise patterns, so it is possible to detect noise inconsistencies between authentic and tampered regions, especially if imagery has been combined from two or more photos.

Swapping Autoencoder for Deep Image Manipulation:

The Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image.

There thousands of application of Artificial Intelligence and Machine Learning which has ruled over the market.

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Raj Kumar Vishwakarma