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APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
[article]
2020
arXiv
pre-print
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Physical adversarial attacks threaten to fool object detection systems, but reproducible research on the real-world effectiveness of physical patches and how to defend against them requires a publicly ...
This dataset and the described experiments provide a benchmark for future research on the effectiveness of and defenses against physical adversarial objects in the wild. ...
We would also like to thank our MITRE colleagues who participated in collecting and annotating the APRICOT dataset and creating the adversarial patches. ...
arXiv:1912.08166v2
fatcat:b3uekiyoinf6zl2skwgbp2sgg4
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
[article]
2022
arXiv
pre-print
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Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. ...
Furthermore, we present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. ...
To the best of our knowledge, APRI-COT [6] is the only publicly available dataset of physical adversarial attacks on object detectors. ...
arXiv:2112.04532v2
fatcat:aac4akekazbbxftcxsit5yo35q
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
[article]
2022
arXiv
pre-print
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This work presents Z-Mask, a robust and effective strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. ...
The effectiveness of Z-Mask is evaluated with an extensive set of experiments carried out on models for both semantic segmentation and object detection. ...
False detection attacks, as the ones included in the APRICOT dataset [2] , can be created by defining y Adv as a single target detection, defined by a certain class and a certain bounding box (APRICOT ...
arXiv:2203.07341v1
fatcat:ti6xsuxybbabba2pldcngfjflq
Synthetic Dataset Generation for Adversarial Machine Learning Research
[article]
2022
arXiv
pre-print
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Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. ...
In this paper we describe our synthetic dataset generation tool that enables scalable collection of such a synthetic dataset with realistic adversarial examples. ...
The views, opinions and/or findings contained in this report are those of The MITRE Corporation and should not be construed as an official government position, policy, or decision, unless designated by ...
arXiv:2207.10719v1
fatcat:uzrrrniypfcwthgyx3mjamoimu
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
[article]
2022
arXiv
pre-print
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physical adversarial patches, as well as for comparing the performance of different adversarial defense/detection methods. ...
The adversarial patches included in the generated datasets are attached to billboards or the back of a truck and are crafted by using state-of-the-art white-box attack strategies to maximize the prediction ...
To the author's best records, APRICOT [15] is the only publicly available dataset that includes physical-world adversarial patches. However, it can only be used to test 2D object detection models. ...
arXiv:2206.04365v1
fatcat:hretnbed4vc4pcravbdzdwqgem
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
[article]
2022
arXiv
pre-print
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Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. ...
Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. ...
Also, adversarial training reduces Benign and robust AP on the PASCAL VOC object detection dataset. ...
arXiv:2207.01795v2
fatcat:6coqtb3ibvg7fhi6z5xveenvrm
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
[article]
2022
arXiv
pre-print
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To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. ...
We showcase the usefulness of this dataset by testing the effectiveness of the computed patches against 127 models. ...
of Upper Austria in the frame of the COMET Programme managed by FFG in the COMET Module S3AI. ...
arXiv:2203.04412v1
fatcat:soouclmvbvd6xlzevzxpnbf4mi
Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques
2022
Mathematical Problems in Engineering
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While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited ...
Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset "Fruit 360" to make it easier for beginner researchers in the field of agriculture ...
Guragai for the useful discussion of the study. ...
doi:10.1155/2022/9210947
fatcat:tm4ivc3pbrc5npthxs7oicquwa
The MENARA booklet for Academia
[article]
2019
Zenodo
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The MENARA Booklets are a series of publications, created under the MENARA Project framework, which provide insights on the Middle East and North Africa regional order. ...
In each of the four Booklets you may find a compendium of articles and extracts covering the most pressing issues for your field of expertise. ...
Bush in the aftermath of the 9/11 terrorist attacks, which was, in a way, a continuation and implementation of this theory. ...
doi:10.5281/zenodo.2609154
fatcat:ypzsewojbjganis2ytlbw3bmcm