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Found 9 papers, 6 papers with code
Regroup Median Loss for Combating Label Noise
The deep model training procedure requires large-scale datasets of annotated data.
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities.
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models.
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space
In addition, due to the avoidance of using surrogate models' gradient information when optimizing AEs for black-box models, our proposed DifAttack inherently possesses better attack capability in the open-set scenario, where the training dataset of the victim model is unknown.
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition
In this work, we propose a novel and effective defense mechanism termed the Universal Defensive Underpainting Patch (UDUP) that modifies the underpainting of text images instead of the characters.
Optical Character Recognition
Optical Character Recognition (OCR)
Robust Image Forgery Detection Over Online Social Network Shared Images
To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed.
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services
Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them.
Self-Supervised Adversarial Example Detection by Disentangled Representation
To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure.
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors.
