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Adobe to use AI for spotting image manipulation

Adobe

27 June 2018, India:

Adobe announced that it has developed a tool to detect image manipulation which are not usually visible to the naked eye.

Vlad Morariu, senior research scientist at Adobe  explained that a variety of tools already exist to help document and trace the digital manipulation of photos.

Vlad said in a blog, “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.”

The research is focused on three common tampering techniques:

splicing – where parts of two different images are combined

copy-move – where objects in a photograph are moved or cloned from one place to another

removal – where an object is removed from a photograph, and filled-in

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,” said Vlad. Although these artifacts are not usually visible to the human eye, they are much more easily detectable through close analysis at the pixel level, or by applying filters that help highlight these changes, the blog added.

Vlad further added, “Using tens of thousands of examples of known, manipulated images, we successfully trained a deep learning neural network to recognize image manipulation, fusing two distinct techniques together in one network to benefit from their complementary detection capabilities.”

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 byproduct 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.

Vlad notes that future work might explore ways to extend the algorithm to include other artifacts of manipulation, such as differences in illumination throughout a photograph or compression introduced by repeated saving of digital files.

Jon Brandt, senior principal scientist and director for Adobe Research, says that answering that question often comes down to trust and reputation rather than technology. “The Associated Press and other news organizations publish guidelines for the appropriate digital editing of photographs for news media,” he explains.

“It’s important to develop technology responsibly, but ultimately these technologies are created in service to society. Consequently, we all share the responsibility to address potential negative impacts of new technologies through changes to our social institutions and conventions.”

(Source – Adobe Blog, Image – Acclaim)

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