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Found 9 papers, 4 papers with code
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications.
Improving the Utility of Differentially Private Clustering through Dynamical Processing
This study aims to alleviate the trade-off between utility and privacy in the task of differentially private clustering.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class.
Parameter-free HE-friendly Logistic Regression
However, current approaches on the training of encrypted machine learning have relied heavily on hyperparameter selection, which should be avoided owing to the extreme difficulty of conducting validation on encrypted data.
Geometrically Adaptive Dictionary Attack on Face Recognition
We apply the GADA strategy to two existing attack methods and show overwhelming performance improvement in the experiments on the LFW and CPLFW datasets.
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks
In this paper, we pay attention to an implicit assumption of query-based black-box adversarial attacks that the target model's output exactly corresponds to the query input.
Robust Federated Learning with Noisy Labels
To solve these problems, we introduce a novel federated learning scheme that the server cooperates with local models to maintain consistent decision boundaries by interchanging class-wise centroids.
BitNet: Learning-Based Bit-Depth Expansion
Since insufficient bit-depth may generate annoying false contours or lose detailed visual appearance, bit-depth expansion (BDE) from low bit-depth (LBD) images to high bit-depth (HBD) images becomes more and more important.
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference
Moreover, by modifying the VQA model’s answer through the output of the NLI model, we show that VQA performance increases by 1. 1% from the original model.
