In a few short years, neural-network-powered automated face swaps have gone from being mildly convincing to eerily believable. But through new research from Disney, neural face-swapping is poised to become a legitimate and high-quality tool for visual effects studios working on Hollywood blockbusters. One of the bigger challenges of creating deepfake videos, as they've come to be known, is creating a vast database of facial images of a person--thousands of different expressions and poses--that can be swapped into a target video. The larger the database and the higher the quality of the images, the better the face swaps will turn out. But the images (which are more often than not headshots of famous people) are usually pulled from sources with limited resolution.
Researchers have found a way to turn simple line drawings into photo-realistic facial images. Developed by a team at the Chinese Academy of Sciences in Beijing, DeepFaceDrawing uses artificial intelligence to help "users with little training in drawing to produce high-quality images from rough or even incomplete freehand sketches." This isn't the first time we've seen tech like this (remember the horrifying results of Pix2Pix's autofill tool?), but it is certainly the most advanced to date, and it doesn't require the same level of detail in source sketches as previous iterations have. It works largely through probability -- instead of requiring detailed eyelid or lip shapes, for example, the software refers to a database of faces and facial components, and considers how each facial element works with each other. Eyes, nose, mouth, face shape and hair type are all considered separately, and then assembled into a single image. As the paper explains, "Recent deep image-to-image translation techniques allow fast generation of face images from freehand sketches.
Here we have a compilation of our course focuses on Image recognition and manipulation alongside Machine Learning, What you'll learn Build a facial recognition project Develop an interface that will allow you to load, modify, and save CIImages. Build a simple digit recognition project using the MNIST handwritten digit database Use Facial Recognition software that is available in Swift to detect facial features such as eyes and smiles in photographs. Build a simple image recognition project using the CIFAR-10 library Description Here we have a compilation of our course focuses on Image recognition and manipulation alongside Machine Learning, in this era of AI starting to learn how to recognize Images, using this course you can get ahead of the game before anyone else! First we will install PyCharm 2017.2.3 and explore the interface. I will show you every step of the way. You will learn crucial Python 3.6.2
Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.
As billions of personal data such as photos are shared through social media and network, the privacy and security of data have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information with the aid of image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against real-world recognition systems. In this paper, we argue that an algorithm for privacy protection must block the ability of automatic inference of the identity and at the same time, make the resultant image natural from the users' point of view. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM), which can generate natural face images by adding adversarial identity masks to conceal ones' identity against a recognition system. Extensive experiments on various state-of-the-art face recognition models demonstrate the effectiveness of our proposed method on alleviating the identity leakage of face images, without sacrificing the visual quality of the protected images.
Until January few had heard of Clearview AI, a company that has scraped billions of publicly available images from millions of websites in order to build a facial image search engine app. Clearview claims that more than six hundred law enforcement agencies have used its technology in the last year. News that police officers can search against a plethora of images uploaded to the most popular social media platforms has prompted outcry from officials, activists, and civil libertarians. Clearview's technology should concern everyone who values privacy and security. Clearview CEO Hoan Ton-That has been on the defensive since a New York Times report raised the company's profile from relative obscurity to the topic of a nationwide privacy discussion.
Read the paper to learn more about Kaokore dataset, our motivations in making them, as well as creative usage of it! KaoKore is a novel dataset of face images from Japanese illustrations along with multiple labels for each face, derived from the Collection of Facial Expressions. KaoKore dataset is build based on the Collection of Facial Expressions, which results from an effort by the ROIS-DS Center for Open Data in the Humanities (CODH) that has been publicly available since 2018. It provides a dataset of cropped face images extracted from Japanese artworks publicly available from National Institute of Japanese Literature, Kyoto University Rare Materials Digital Archive and Keio University Media Center from the Late Muromachi Period (16th century) to the Early Edo Period (17th century) to facilitate research into art history, especially the study of artistic style. It also provides corresponding metadata annotated by researchers with domain expertise.
Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy. Traditional methods for face de-identification wipe out facial information entirely, making it impossible to analyze facial behavior. Recent advancements on whole-body keypoints detection also rely on facial input to estimate body keypoints. Both facial and body keypoints are critical in some medical diagnoses, and keypoints invariability after de-identification is of great importance. Here, we propose a solution using deepfake technology, the face swapping technique. While this swapping method has been criticized for invading privacy and portraiture right, it could conversely protect privacy in medical video: patients' faces could be swapped to a proper target face and become unrecognizable. However, it remained an open question that to what extent the swapping de-identification method could affect the automatic detection of body keypoints. In this study, we apply deepfake technology to Parkinson's disease examination videos to de-identify subjects, and quantitatively show that: face-swapping as a de-identification approach is reliable, and it keeps the keypoints almost invariant, significantly better than traditional methods. This study proposes a pipeline for video de-identification and keypoint preservation, clearing up some ethical restrictions for medical data sharing. This work could make open-source high quality medical video datasets more feasible and promote future medical research that benefits our society.
Numerous recent studies have demonstrated how Deep Neural Network (DNN) classifiers can be fooled by adversarial examples, in which an attacker adds perturbations to an original sample, causing the classifier to misclassify the sample. Adversarial attacks that render DNNs vulnerable in real life represent a serious threat, given the consequences of improperly functioning autonomous vehicles, malware filters, or biometric authentication systems. In this paper, we apply Fast Gradient Sign Method to introduce perturbations to a facial image dataset and then test the output on a different classifier that we trained ourselves, to analyze transferability of this method. Next, we craft a variety of different attack algorithms on a facial image dataset, with the intention of developing untargeted black-box approaches assuming minimal adversarial knowledge, to further assess the robustness of DNNs in the facial recognition realm. We explore modifying single optimal pixels by a large amount, or modifying all pixels by a smaller amount, or combining these two attack approaches. While our single-pixel attacks achieved about a 15% average decrease in classifier confidence level for the actual class, the all-pixel attacks were more successful and achieved up to an 84% average decrease in confidence, along with an 81.6% misclassification rate, in the case of the attack that we tested with the highest levels of perturbation. Even with these high levels of perturbation, the face images remained fairly clearly identifiable to a human. We hope our research may help to advance the study of adversarial attacks on DNNs and defensive mechanisms to counteract them, particularly in the facial recognition domain.
Is thermal imagery detailed enough to enable an AI model to recognize people's facial features? That's the question Intel and Gánsk University of Technology researchers sought to answer in a study recently presented at the Institute of Electrical and Electronics Engineers' 12th International Conference on Human System Interaction. These researchers investigated the performance of a model trained on visible light data that was subsequently retrained on thermal images. As the researchers point out in a paper describing their work, thermal imagery is often used in lieu of RGB camera data within environments where privacy is preferred or otherwise mandated, like medical facilities. That's because it's able to obscure personally identifying details like eye color and jaw line.