Next, we are going to cover several stages in one step, all are related to each other and it will make more sense. Previously we have seen the methods for signaling the beginning of a run and of an epoch. In every epoch, we have to iterate over the entire dataset, for various reasons related to both computational and convergence optimizations we do so in batches (mini-batch stochastic gradient descent). As such we have to log some information related to this step to our RunManager, which will help us to compute the overall loss and the accuracy for the entire epoch. Once the epoch finishes we have to compute the aforementioned metrics, save them to the local collection and log them to Tensorboard.

In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.