# face recognition

Face recognition is the process of verifying or identifying individuals by their faces, including automated identification and verification operations.

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## Hacking FaceNet using Adversarial examples

DevFeed: [Hacking FaceNet using Adversarial examples](<https://devfeed.tech/articles/hacking-facenet-using-adversarial-examples-21534.md>)

Original publisher: [Read original article](<http://lifepluslinux.blogspot.com/2018/01/hacking-facenet-using-adversarial.html>)

Author: Suresh Alse (noreply@blogger.com)

Published: 2018-01-11T23:29:00Z

Content type: tutorial

Language: en

Sources: [Life Plus Linux](<https://devfeed.tech/sources/life-plus-linux.md>)

Topics: [face recognition](<https://devfeed.tech/topics/face-recognition.md>), [Machine Learning, Security Attacks](<https://devfeed.tech/topics/machine-learning-security-attacks.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>)

Tags: [authentication](<https://devfeed.tech/tags/authentication.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deeplearning](<https://devfeed.tech/tags/deeplearning.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [face-recognition](<https://devfeed.tech/tags/face-recognition.md>), [hacking](<https://devfeed.tech/tags/hacking.md>), [ml](<https://devfeed.tech/tags/ml.md>), [neural](<https://devfeed.tech/tags/neural.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

This tutorial explains how FaceNet performs face recognition using embeddings and triplet loss, then demonstrates generating small adversarial noise intended to make an attacker's photo be identified as a target.

### Source excerpt

With the rise in popularity of face recognition systems with deep learning and it's application in security/ authentication, it is important to make sure that it is not that easy to fool them. I recently finished the 4th course on deeplearning.ai where there is an assignment which asks us to build a face recognition system - FaceNet. While I was working on the assignment, I couldn't stop thinking about how easy it is to fool it with adversarial examples. In this post I will tell you how I managed to do it. First off, some basics about FaceNet. Unlike image recognition systems which map every image with a class, it is not possible to assign a class label to every face in face recognition. This is because one, there are way too many faces that a system should handle in the real world to assign class to each of them and two, if there are new people the system should handle, it can't do it. So, what we do is, we build a system that learns similarities and dissimilarities. Basically, there is a neural network similar to what we have in image recognition and instead of applying softmax in the end, we just take the logits as embedding for the given image input and then minimize something called the triplet loss. Consider face A, we have a positive match P and negative match N. If f is the embedding function and L is the triplet loss, we have this: Triplet loss Basically, it is incentivizing small distance between A - P and large distance between A - N. Also, I really recommend watching Ian Goodfellow's lecture from Stanford's CS231n course if you want to know about adversarial examples. Like I said earlier, this thought came to me while doing an assignment from 4th course from deeplearning.ai which can be found here and I have built on top of it. The main idea here is to find small noise that when added to someone's photo although causing virtually no visual changes, can make faceNet identify them as the target. Benoit (attacker) Add noise Kian Kian Actual (Target) First l