Ghost Attack: Using 19th-Century Illusions to Fool 21st-Century Biometrics

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   Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study

   Dinh-Luan Nguyen, S. S. Arora, Yuhang Wu et al. · 2020 · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · Not summarized yet

   Abstract

   Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel. In such setting, physical attacks that directly provide a malicious input through the input channel pose a bigger threat. We investigate the feasibility of conducting real-time physical attacks on face recognition systems using adversarial light projections. A setup comprising a commercially available web camera and a projector is used to conduct the attack. The …

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   FaceForensics++: Learning to Detect Manipulated Facial Images

   Andreas Rössler, Davide Cozzolino, Luisa Verdoliva et al. · 2019 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives f…

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   Deep Face Representations for Differential Morphing Attack Detection

   U. Scherhag, C. Rathgeb, J. Merkle et al. · 2020 · IEEE Transactions on Information Forensics and Security · Not summarized yet

   Abstract

   The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of s…

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   Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

   Eklavya Sarkar, P. Korshunov, Laurent Colbois et al. · 2020 · arXiv.org · Not summarized yet

   Abstract

   Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (StyleGAN), generated with original face images from three public face datasets. We also conduct extensive experiments …

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   Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect

   Nesli Erdogmus, S. Marcel · 2013 · International Conference on Biometrics: Theory, Applications and Systems · Not summarized yet
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   Are GAN-based morphs threatening face recognition?

   Eklavya Sarkar, P. Korshunov, Laurent Colbois et al. · 2022 · IEEE International Conference on Acoustics, Speech, and Signal Processing · Not summarized yet

   Abstract

   Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also cond…

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   MADation: Face Morphing Attack Detection with Foundation Models

   Eduarda Caldeira, Guray Ozgur, Tahar Chettaoui et al. · 2025 · 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · Not summarized yet

   Abstract

   Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such …

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   Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking

   Ilke Demir, Umur A. Ciftci · 2021 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Following the recent initiatives for the democratization of AI, deep fake generators have become increasingly popular and accessible, causing dystopian scenarios towards social erosion of trust. A particular domain, such as biological signals, attracted attention towards detection methods that are capable of exploiting authenticity signatures in real videos that are not yet faked by generative approaches. In this paper, we first propose several prominent eye and gaze features that deep fakes exhibit differently. Second, we compile those features into signatures and analyze and compare those of real and fake videos, formulating geometric, visual, metric, temporal, and spectral variations. Thi…

Prior work it builds on

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   MagShadow: Physical Adversarial Example Attacks via Electromagnetic Injection

   Zi-Wei Liu, Feng Lin, Zhong-Jie Ba et al. · 2025 · IEEE Transactions on Dependable and Secure Computing · Not summarized yet

   Abstract

   Physical adversarial examples (AEs) have become an increasing threat to deploying deep neural network (DNN) models in the real world. Popular approaches adopt sticking-based or projecting-based strategies that stick the printed adversarial patches to objects or directly project the AE onto objects. Although effective, these methods require access to target objects and generate visible artifacts, which reduces the attack's stealthiness. In this article, we propose MagShadow, a new attack vector that leverages imperceptible electromagnetic (EM) signals to realize physical AEs. MagShadow utilizes the CCD camera sensor's susceptibility to EM injection attacks and induces fine-grained adversarial…

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   Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection

   Laurent Colbois, Sébastien Marcel · 2024 · 2024 IEEE International Joint Conference on Biometrics (IJCB) · Not summarized yet

   Abstract

   Morphing attacks have diversified significantly over the past years, with new methods based on generative adversarial networks (GANs) and diffusion models posing substantial threats to face recognition systems. Recent research has demonstrated the effectiveness of features extracted from large vision models pretrained on bonafide data only (attack-agnostic features) for detecting deep generative images. Building on this, we investigate the potential of these image representations for morphing attack detection (MAD). We develop supervised detectors by training a simple binary linear SVM on the extracted features and one-class detectors by modeling the distribution of bonafide features with a …

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   The Replay-Mobile Face Presentation-Attack Database

   Artur Costa-Pazo, S. Bhattacharjee, Esteban Vázquez-Fernández et al. · 2016 · Biometrics and Electronic Signatures · Not summarized yet

   Abstract

   For face authentication to become widespread on mobile devices, robust countermeasures must be developed for face presentation-attack detection (PAD). Existing databases for evaluating face-PAD methods do not capture the specific characteristics of mobile devices. We introduce a new database, REPLAY-MOBILE, for this purpose.1 This publicly available database includes 1,200 videos corresponding to 40 clients. Besides the genuine videos, the database contains a variety of presentation-attacks. The database also provides three non- overlapping sets for training, validating and testing classifiers for the face-PAD problem. This will help researchers in comparing new approaches to existing algori…

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   CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

   Shifeng Zhang, Ajian Liu, Jun Wan et al. · 2019 · IEEE Transactions on Biometrics Behavior and Identity Science · Not summarized yet

   Abstract

   Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (…

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   MagFace: A Universal Representation for Face Recognition and Quality Assessment

   Qiang Meng, Shichao Zhao, Zhida Huang et al. · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 · AI summary available

   Abstract

   The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples…