2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures

• URL: http://arxiv.org/abs/2409.04982v1
• Date: Sun, 8 Sep 2024 05:35:05 GMT
• Title: 2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
• Authors: Xinheng Xie, Kureha Yamaguchi, Margaux Leblanc, Simon Malzard, Varun Chhabra, Victoria Nockles, Yue Wu,
• Abstract summary: This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect. We show both superior performance and a reduction in the time to detect the presence of adversarial perturbations in images.
• Score: 2.6642754249961103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models performing image-related tasks, e.g. detection, and classification, are vulnerable to adversarial attacks that can degrade their performance and produce undesirable outcomes. This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect, which uses a 2D-signature-embedded semi-supervised framework rooted in rough path theory. We demonstrate our method in adversarial settings for training-time and test-time attacks, and benchmark our framework against other state of the art methods. Using 2DSig-Detect for anomaly detection, we show both superior performance and a reduction in the computation time to detect the presence of adversarial perturbations in images.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• 2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems [8.717726409183175]
  We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Unlike traditional additive noise approaches, 2D-Malafide optimises a small number of filter coefficients to generate robust adversarial perturbations. Experiments, conducted using the FaceForensics++ dataset, demonstrate that 2D-Malafide substantially degrades detection performance in both white-box and black-box settings.
  arXiv  Detail & Related papers  (2024-08-26T09:41:40Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations [13.529124221397822]
  We introduce a novel introspection solution for 2D object detection based on Deep Neural Networks (DNNs) We implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.
  arXiv  Detail & Related papers  (2024-03-02T10:56:14Z)
• SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes [7.553507857251396]
  We propose a novel deepfake detector, called SeeABLE, that formalizes the detection problem as a (one-class) out-of-distribution detection task. SeeABLE pushes perturbed faces towards predefined prototypes using a novel regression-based bounded contrastive loss. We show that our model convincingly outperforms competing state-of-the-art detectors, while exhibiting highly encouraging generalization capabilities.
  arXiv  Detail & Related papers  (2022-11-21T09:38:30Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images [0.7734726150561086]
  Conditional Variational AutoEncoders (CVAE) are surprisingly good at detecting imperceptible image perturbations. We show how CVAEs can be effectively used to detect adversarial attacks on image classification networks.
  arXiv  Detail & Related papers  (2021-11-28T20:36:27Z)
• Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving [87.3492357041748]
  In this paper, we showcase practical susceptibilities of multi-sensor detection by placing an adversarial object on top of a host vehicle. Our experiments demonstrate that successful attacks are primarily caused by easily corrupted image features. Towards more robust multi-modal perception systems, we show that adversarial training with feature denoising can boost robustness to such attacks significantly.
  arXiv  Detail & Related papers  (2021-01-17T21:15:34Z)
• Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera [83.31666463259849]
  We propose a method to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. We show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained using manual annotations. Our method is an effective way to improve person detectors during deployment without any additional labeling effort.
  arXiv  Detail & Related papers  (2020-12-16T12:10:04Z)

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.