![]() ![]() ![]() Without solid ways to validate that the information is accurate, these photographs could pose issues when they are presented as evidence in a case and in court. But unfortunately, in just a few seconds, both visual content and metadata can now be easily manipulated to create credible fakes. Modern digital images are mines of information: in addition to the visual content, metadata often contain precise information about when, where and who captured the image. And thanks to social media and the prevalence of high-quality mobile phone cameras, there is a dramatic increase of images submitted as evidence by citizens and witnesses, to law enforcement agencies. With the constant development and introduction of new digital technologies, digital images are now key sources of evidence for investigations. ![]() Amped Authenticate is used by digital forensic experts and intelligence agencies worldwide. Amped Authenticate provides a suite of powerful tools to determine whether an image is an unaltered original, an original generated by a specific device, or the result of manipulation using a photo editing software, making its admissibility as evidence questionable. It allows to carry out a thorough forensic analysis with numerous filters, and produce a customizable forensic report ensuring that your results are well explained and reproducible.Īmped Authenticate is the leading forensic software for unveiling the processing history of a digital image. This is what Amped Authenticate is all about. These traces can be leveraged to reconstruct the processing history, identify the image's originating device, and possibly localize manipulated regions. Image forensics is based on a fundamental idea: every processing step in the life-cycle of an image leaves some distinctive traces in the image itself. Amped Authenticate provides a very user-friendly interface that allows to open an image in the most common formats (JPEG, HEIF, PNG, TIFF, BMP, etc.) and easily select several filters and tools to perform diverse tests on the image. ![]()
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