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Explainable AI for Natural Adversarial Images
[article]
2021
arXiv
pre-print
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Here we evaluate if methods from explainable AI can disrupt this assumption to help participants predict AI classifications for adversarial and standard images. ...
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. ...
Here we test whether explanations help people to predict misclassifications of natural adversarial images, which is an essential prerequisite for effective human oversight of AI systems. ...
arXiv:2106.09106v1
fatcat:huvnzephw5bv5fo5mq2rja76ri
Overlooked Trustworthiness of Explainability in Medical AI
[article]
2021
medRxiv
pre-print
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AI researchers, practitioners, and authoritative agencies in the medical domain should use caution when explaining AI models because such an explanation could be irrelevant, misleading, and even adversarially ...
While various methods have been proposed to explain AI models, the trustworthiness of the generated explanation received little examination. ...
Saliency maps, also commonly referred to as heat maps, are the most commonly used method for AI explainability 6 . ...
doi:10.1101/2021.12.23.21268289
fatcat:wg5rbtzmafg7xcmfu5nzuknkte
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
[article]
2022
arXiv
pre-print
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natural or adversarial (i.e., contains adversarial perturbations). ...
Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. ...
Figure 2 : 3 - 23 Figure 2: 3-NN is consistently among the most effective in improving human-AI team accuracy (%) on natural ImageNet images (a), natural Dog images (b), and adversarial ImageNet images ...
arXiv:2105.14944v5
fatcat:iqcul3btbzgepgzdr7drkltrtu
Trustworthy AI: A Computational Perspective
[article]
2021
arXiv
pre-print
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In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability ...
In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. ...
For training in adversarial defense methods, an important issue is the robust overfitting and lack of generalization in both adversarial and natural examples. ...
arXiv:2107.06641v3
fatcat:ymqaxvzsoncqrcosj5mxcvgsuy
An Assessment of Robustness for Adversarial Attacks and Physical Distortions on Image Classification using Explainable AI
2021
SGAI Conferences
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As a result, research on model interpretability and explainability has been carried out in the domain which is collectively known as Explainable AI. ...
Using a set of Explainable AI techniques, this study is investigating the deep learning networks' robustness; i.e., the decision-making process in neural networks and important pixel attributes for the ...
Suresha Perera (Lecturer at the Open University Sri Lanka) for proofreading and guiding the revisions to this paper. ...
dblp:conf/sgai/MahimaAP21
fatcat:rdwwpu3eufcujarr4m7uh2a7ty
Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions
[article]
2022
arXiv
pre-print
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We found that 1) Defence techniques for adversarial attacks on AI are available for specific kind of attacks and even adversarial training is AI based technique which in further prone to different attacks ...
A global solution for all sort of adversarial attacks on AI based healthcare. However, this technique has significant limits and challenges that need to be addressed in future studies. ...
Adversarial images mislead DNN as a minute perturbation in the input makes DNN vulnerable. The Figure 13 represents the approaches for an adversarial attack on an image. ...
arXiv:2206.04793v1
fatcat:v2wrluwugja65btmjct5wlrfm4
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
[article]
2020
arXiv
pre-print
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Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. ...
In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for perceptual similarity ...
Informed consent was obtained from all patients for being included in the study. ...
arXiv:1812.09431v3
fatcat:k4ps4yljcbexthcul4r36gmahy
Ten AI Stepping Stones for Cybersecurity
[article]
2019
arXiv
pre-print
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the implications of the adversarial nature of cybersecurity in the learning techniques. ...
We then discuss the use of AI by attackers on a level playing field including several issues in an AI battlefield, and an AI perspective on the old cat-and-mouse game including how the adversary may assess ...
In fact, explainability and resilience to adversarial attacks are two of of the challenges identified by UC Berkeley [40] for mission critical AI usage (which includes cybersecurity), together with continuous ...
arXiv:1912.06817v1
fatcat:ujeitni5grcopbhl6dwenncjje
Algorithms in future insurance markets
2021
International Journal of Data Science and Big Data Analytics
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., long short-term memory, generative adversarial networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). ...
These technologies are important since they underpin the automation of the insurance markets and risk analysis, and provide the context for the algorithms, such as AI machine learning and computational ...
fairness), and (b) those related to the dynamic nature of AI. ...
doi:10.51483/ijdsbda.1.1.2021.1-19
fatcat:gty5qdugnbhm3mophojqyxmkja
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2012.09110v2 fatcat:5ki3bibid5as7nq7ztma5lja64Other Versions
Globally there are calls for technology to be made more humane and human-centered. ...
We believe such rigorous analysis will provide a baseline for future research in the domain. ...
" in nature. ...
arXiv:2012.09110v4
fatcat:yxh5tvpehbgldcblweoovbvdsq
Robustness of different loss functions and their impact on networks learning capability
[article]
2021
arXiv
pre-print
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For our case, we will use two sets of loss functions, generalized loss functions like Binary cross-entropy or BCE and specialized loss functions like Dice loss or focal loss. ...
In order to establish the difference between generalized loss and specialized losses, we will train several models using the above-mentioned losses and then compare their robustness on adversarial examples ...
Explainability in AI leads to Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI solutions in real-life scenarios with fairness, explainability and accountability ...
arXiv:2110.08322v2
fatcat:qzruirgisrgjbgnwfen64fep7m
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
2019
Zenodo
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In this research, we present a novel detection method that uses Shapley Additive Explanations (SHAP) values computed for the internal layers of a DNN classifier to discriminate between normal and adversarial ...
and adversarial inputs. ...
We try to leverage this hypothesized property of adversarial examples by utilizing explainable AI methods (XAI) for interpreting model predictions. ...
doi:10.5281/zenodo.3941781
fatcat:xea2tuzzmngtrgjvorhwqky5lm
Towards Resilient Artificial Intelligence: Survey and Research Issues
[article]
2021
arXiv
pre-print
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2021
2021 IEEE International Conference on Cyber Security and Resilience (CSR)
unpublished
doi:10.1109/csr51186.2021.9527986 fatcat:lgty4bnk6nf6fp54vhz3fhakme
doi:10.1109/csr51186.2021.9527986 fatcat:lgty4bnk6nf6fp54vhz3fhakme
Considering the particular nature of AI, and machine learning (ML) in particular, this paper provides an overview of the emerging field of resilient AI and presents research issues the authors identify ...
Their resilience against attacks and other environmental influences needs to be ensured just like for other IT assets. ...
Due to the explainability problem, even proper annotation of the changes made does not fully explain the impact on AI-based decisions; thus the question of responsibility for errors and negative effects ...
arXiv:2109.08904v1
fatcat:vadq2vohljhxpbcokklir4buee
A Review on Explainability in Multimodal Deep Neural Nets
2021
IEEE Access
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This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. ...
INDEX TERMS deep multimodal learning, explainable AI, interpretability, survey, trends, vision and language research, XAI. ...
AI (XAI) approaches for the Natural Language Processing domain. ...
doi:10.1109/access.2021.3070212
fatcat:5wtxr4nf7rbshk5zx7lzbtcram
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
[article]
2020
arXiv
pre-print
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In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. ...
We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach ...
Images from CIFAR10 dataset are easily explained due to the nature of the dataset with most objects in the images being located in the middle of the image and the lack of noisy background in most images ...
arXiv:2005.10284v4
fatcat:7p3pi6qn75eoxlpdgflumam4hi
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