Deeptector

Deeptector.io detects DeepFakes using the power of the same Artifical Intelligence methods that generate state-of-the-art DeepFakes in the first place. These algorithms are commonly known as deep neural networks, and they train themselves to differentiate between real videos and fake ones by looking at thousands of examples of each. Deeptector.io is trained on research-grade DeepFakes to ensure that it can detect the most sophisticated material. The neural network at the core of Deeptector.io functionality can pick out differences between real and fake videos that are invisible to the human eye, and it can do so with an overall accuracy of over 90 percent. When you upload a video to Deeptector.io, the video is passed through a convolutional nerual network one frame at a time. The neural network produces a set of features for each frame and identifies fake sections of video by looking at sequences of features, which it learns by examining over 14 million images that are publicly available in the ImageNet dataset.

The Deeptector team is a group of computer science and journalism graduate students who are passionate about artificial intelligence, machine learning, and computer vision.