In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called "3DG-GA", Deep De-identified anonymous Dataset Generation, uses Genetics Algorithm as a strategy for synthetic faces generation. The algorithm includes GAN artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving, the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, which can reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions.
Purdue University（普渡大学）；University of Rochester（罗切斯特大学）
With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to addre…
Dublin City University（都柏林城市大学）；Insight Centre for Data Analytics（洞察数据分析中心）
Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system. Such abuse of images is in violation of privacy rights but is difficult to counter. It is well established that adversarial example images can be created for recognition systems which are based on deep neural networks. These adversarial examples can be used to disrupt the utility of the images as reference examples or training data. In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an …
Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However, existing methods only focus on detecting one-step facial manipulation. As the emergence of easy-accessible facial editing applications, people can easily manipulate facial components using multi-step operations in a sequential manner. This new threat requires us to detect a sequence of facial manipulations, which is vital for both detecting deepfake media and re…
SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacy
School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；School of Computer Science, Engineering Research Center of Machine Learning and Industry Intelligence, Sichuan University（四川大学计算机科学学院，机器学习与工业智能工程研究中心）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；College of Artificial Intelligence, Southwest University（西南大学人工智能学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学科技学院计算机与信息科学系）；Data61, CSIRO（CSIRO Data61）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS)…
SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation
Ping An Technology（平安科技）；Southeast University（东南大学）；Sun Yat-sen University（中山大学）；Nanjing University of Science and Technology（南京理工大学）；East China University of Science and Technology（华东理工大学）；University of Shanghai for Science and Technology（上海理工大学）
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）
Broad Institute of MIT and Harvard（麻省理工学院-哈佛大学博德研究所）；Korea University（高丽大学）；NAVER CLOVA；Harvard Medical School（哈佛医学院）；Ophthalmic Consultants of Boston（波士顿眼科顾问诊所）
The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personally-identifying nature of retinal images, encompassing the unique vascular structure of each individual, often prevents this data from being shared openly. While prior works have explored image de-identification strategies based on synthetic averaging of im…
SenseTime Research（商汤科技研究院）；University of Electronic Science and Technology of China（电子科技大学）；College of Software, Beihang University（北京航空航天大学软件学院）；Northwestern Polytechnical University（西北工业大学）
As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of frequency could be a cure, as frequency provides a complementary viewpoint where either subtle forgery artifacts or compression errors could be well descr…
The precise age estimation of child sexual abuse and exploitation (CSAE) victims is one of the most significant digital forensic challenges. Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. The main priority - safeguarding children -- is often negatively impacted by a huge forensic backlog, cognitive bias and the immense psychological stress that this work can entail. This paper evaluates existing facial image datasets and proposes a new dataset tailored to the needs of similar digital …
