Posted on 6/23/2018, 10:30:21 AM by BenLurkin
Think of sharks swimming in the streets of New Jersey after Hurricane Sandy, or someone flying a “where’s my damn dinner?” banner over a women’s march. Those images were fake, but clever manipulation can trick news outlets and social media users into thinking they’re real.
Whenever someone alters an image, unless they are pixel perfect in their work, they always leave behind indicators that the photo is modified. Metadata and watermarks can help determine a source image, and forensics can probe factors like lighting, noise distribution and edges on the pixel level to find inconsistencies. If a color is slightly off, for instance, forensic tools can flag it. But Adobe wagers that it could employ AI to find telltale signs of manipulation faster and more reliably.
The AI looks for three types of manipulation: cloning, splicing and removal. Cloning (or copy-move) is when objects are copied or moved within an image, such as parts of a crowd duplicated to make it seem like there are more people in a scene. Splicing is where someone smushes together aspects of two different images, like the aforementioned sharks, which were grabbed from one photo and blended into another showing flooded streets.
As is typical with machine learning methods, the Adobe team, along with University of Maryland researchers, fed the AI tens of thousands of phony images to teach it what to look for….
The AI uses a pair of techniques to hunt for artifacts. It looks for changes to the red, green and blue color values of pixels. It also examines noise, the random variations of color and brightness caused by a camera’s sensor or software manipulations. Those noise patterns are often unique to cameras or photos, so the AI can pick up on inconsistencies, especially in spliced images.
(Excerpt) Read more at engadget.com …