Find the Differences: Differential Morphing Attack Detection vs Face Recognition
- URL: http://arxiv.org/abs/2604.14734v2
- Date: Thu, 23 Apr 2026 10:11:44 GMT
- Title: Find the Differences: Differential Morphing Attack Detection vs Face Recognition
- Abstract summary: We argue that face recognition and differential morphing attack detection (D-MAD) in principle perform very similar tasks.<n>We show that currently used decision thresholds inherently lead to FR systems being vulnerable to morphing attacks.<n>We propose using FR systems that are already in place for morphing detection and introduce a new evaluation threshold that guarantees an upper limit to the vulnerability to morphing attacks.
- Score: 3.4709432024347247
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Morphing is a challenge to face recognition (FR) for which several morphing attack detection solutions have been proposed. We argue that face recognition and differential morphing attack detection (D-MAD) in principle perform very similar tasks, which we support by comparing an FR system with two existing D-MAD approaches. We also show that currently used decision thresholds inherently lead to FR systems being vulnerable to morphing attacks and that this explains the tradeoff between performance on normal images and vulnerability to morphing attacks. We propose using FR systems that are already in place for morphing detection and introduce a new evaluation threshold that guarantees an upper limit to the vulnerability to morphing attacks - even of unknown types.
Related papers
- R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection [6.348134327816868]
Face morphing attacks pose a substantial risk to the reliability of face recognition systems.<n>This paper presents a new Single-Image Face Morphing Attack Detection framework.<n>It integrates high-frequency Laplacian residual statistics with representations from a frozen, foundation-scale vision transformer.
arXiv Detail & Related papers (2026-04-19T08:21:45Z) - AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries.
We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots.
We also show that ACPT is robust to 3 types of adaptive attacks.
arXiv Detail & Related papers (2024-08-04T09:53:50Z) - MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models.
Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs.
Empirical evaluations conducted on different datasets validate the efficacy of our approach.
arXiv Detail & Related papers (2024-06-13T15:55:04Z) - On the Robustness of Large Multimodal Models Against Image Adversarial
Attacks [81.2935966933355]
We study the impact of visual adversarial attacks on Large Multimodal Models (LMMs)
We find that in general LMMs are not robust to visual adversarial inputs.
We propose a new approach to real-world image classification which we term query decomposition.
arXiv Detail & Related papers (2023-12-06T04:59:56Z) - Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy.
Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
arXiv Detail & Related papers (2023-10-18T14:49:54Z) - Fused Classification For Differential Face Morphing Detection [0.0]
Face morphing, a presentation attack technique, poses significant security risks to face recognition systems.
Traditional methods struggle to detect morphing attacks, which involve blending multiple face images.
We propose an extended approach based on fused classification method for no-reference scenario.
arXiv Detail & Related papers (2023-09-01T16:14:29Z) - Leveraging Diffusion For Strong and High Quality Face Morphing Attacks [2.0795007613453445]
Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities.
We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image.
arXiv Detail & Related papers (2023-01-10T21:50:26Z) - Reliable Face Morphing Attack Detection in On-The-Fly Border Control
Scenario with Variation in Image Resolution and Capture Distance [3.6833521970861685]
Face morphing attacks are highly potential in deceiving automatic FRS and human observers.
We present a novel Differential-MAD (D-MAD) algorithm based on the spherical distances and hierarchical fusion of deep features.
Experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset.
arXiv Detail & Related papers (2022-09-30T13:58:43Z) - Asymmetric Modality Translation For Face Presentation Attack Detection [55.09300842243827]
Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users.
We propose a novel framework based on asymmetric modality translation forPAD in bi-modality scenarios.
Our method achieves state-of-the-art performance under different evaluation protocols.
arXiv Detail & Related papers (2021-10-18T08:59:09Z) - Robustness of Facial Recognition to GAN-based Face-morphing Attacks [3.3734683025502723]
We identify two new, GAN-based methods that an attacker may already have in his arsenal.
Each method is evaluated against state-of-the-art facial recognition (FR) algorithms.
We demonstrate that improvements to the fidelity of FR algorithms do lead to a reduction in the success rate of attacks provided morphed images.
arXiv Detail & Related papers (2020-12-18T23:06:20Z) - Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition [56.844587127848854]
We demonstrate that the state-of-the-art gait recognition model is vulnerable to such attacks.
We employ a generative adversarial network based architecture to semantically generate adversarial high-quality gait silhouettes or video frames.
The experimental results show that if only one-fortieth of the frames are attacked, the accuracy of the target model drops dramatically.
arXiv Detail & Related papers (2020-02-22T10:08:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.