Results 71 to 80 of about 24,885,017 (322)
Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
, 2019 Kazuya Kakizaki and Kosuke Yoshida share equal contributions. Accepted at AAAI Workshop on Artificial Intelligence Safety (2020)
Kakizaki, Kazuya, Yoshida, Kosuke
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A Survey on Adversarial Recommender Systems
ACM Computing Surveys, 2021 Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: Many applications ...
Deldjoo, Yashar +2 more
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Neurocomputing, 2020 Deep neural networks (DNNs) display good performance in the domains of recognition and prediction, such as on tasks of image recognition, speech recognition, video recognition, and pattern analysis.
Hyun Kwon, H. Yoon, Ki-Woong Park
semanticscholar +1 more source
Information Transmission Strategies for Self‐Organized Robotic Aggregation
Advanced Robotics Research, EarlyView.In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Adversarial Robustness by One Bit Double Quantization for Visual Classification
IEEE Access, 2019 In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images ...
Maungmaung Aprilpyone +2 more
doaj +1 more source
Dual-Targeted Textfooler Attack on Text Classification Systems
IEEE Access, 2023 Deep neural networks provide good performance on classification tasks such as those for image, audio, and text classification. However, such neural networks are vulnerable to adversarial examples.
Hyun Kwon
doaj +1 more source
Adversarial Attacks Against Binary Similarity Systems
IEEE AccessIn recent years, binary analysis gained traction as a fundamental approach to inspect software and guarantee its security. Due to the exponential increase of devices running software, much research is now moving towards new autonomous solutions based on deep learning models, as they have been showing state-of-the-art performances in solving binary ...
Capozzi, Gianluca +3 more
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Feature-Guided Black-Box Safety Testing of Deep Neural Networks
, 2018 Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial examples necessitate some knowledge (architecture, parameters, etc.) of the
B Biggio +8 more
core +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Adversarial Swarms as Dynamical Systems
, 2021 An Adversarial Swarm model consists of two swarms that are interacting with each other in a competing manner. In the present study, an agent-based Adversarial swarm model is developed comprising of two competing swarms, the Attackers and the Defenders, respectively.
Gupta, Soham, Baker, John
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