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Analysis of Adversarial Examples [PDF]
The rise of artificial intelligence (AI) has significantly impacted the field of computer vision (CV). In particular, deep learning (DL) has advanced the development of algorithms that comprehend visual data.
Lorenz, Peter
core +1 more source
Revisiting model fairness via adversarial examples
, 2023 Existing research literally evaluates model fairness over limited observed data. In practice, however, factors such as maliciously crafted examples and naturally corrupted examples often appear in real-world data collection.
Zhang, T +9 more
core +1 more source
3D Printing Innovations in Polymeric Porous and Patterned Architecture
Advanced Functional Materials, EarlyView.Polymeric foams occupy a unique structural space between dense solids and open networks, where engineered void fraction governs mechanical compliance, thermal resistance, and mass transport. Additive manufacturing now enables precise spatial control over cellular architecture, unlocking designer foam structures across applications spanning crash ...
Dhanush Patil +13 more
wiley +1 more source
On the Effect of Adversarial Training Against Invariance-based Adversarial Examples
, 2023 Adversarial examples are carefully crafted attack points that are supposed to fool machine learning classifiers. In the last years, the field of adversarial machine learning, especially the study of perturbation-based adversarial examples, in which a ...
Merkle, Florian +3 more
core +1 more source
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security
Advanced Materials, EarlyView.An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak +4 more
wiley +1 more source
Inverse Design of Amorphous Materials With Targeted Properties
Advanced Materials, EarlyView.AMDEN is a diffusion model framework for the inverse design of amorphous materials with targeted properties. By incorporating Hamiltonian Monte Carlo refinement into the denoising process, the framework overcomes the challenge of generating thermally relaxed disordered structures.
Jonas A. Finkler +4 more
wiley +1 more source
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
Deep neural rejection against adversarial examples [PDF]
, 2020 Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time.
Battista Biggio +6 more
core +1 more source
Contrastive Learning with Adversarial Examples
CoRR, 2020 NeurIPS ...
Chih-Hui Ho, Nuno Vasconcelos
openaire +3 more sources