But whether it's the benign smoothing of a wrinkle in a portrait, or a video manipulated to make it look like a politician saying something offensive, all photo editing leaves traces for the right tools to discover.
Research led by Amit Roy-Chowdhury's Video Computing Group at the University of California, Riverside has developed a deep neural network architecture that can identify manipulated images at the pixel level with high precision. Roy-Chowdhury is a professor of electrical and computer engineering and the Bourns Family Faculty Fellow in the Marlan and Rosemary Bourns College of Engineering.
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A deep neural network is what artificial intelligence researchers call computer systems that have been trained to do specific tasks, in this case, recognize altered images. These networks are organized in connected layers; "architecture" refers to the number of layers and structure of the connections between them.
Objects in images have boundaries and whenever an object is inserted or removed from an image, its boundary will have different qualities than the boundaries of objects in the image naturally. For example, someone with good Photoshop skills will do their best to make the inserted object looks as natural as possible by smoothing these boundaries.
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While this might fool the naked eye, when examined pixel by pixel, the boundaries of the inserted object are different. For example, they are often smoother than the natural objects. By detecting boundaries of inserted and removed objects, a computer should be able to identify altered images.
The researchers labeled nonmanipulated images and the relevant pixels in boundary regions of manipulated images in a large dataset of photos. The aim was to teach the neural network general knowledge about the manipulated and natural regions of photos. They tested the neural network with a set of images it had never seen before, and it detected the altered ones most...
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