Search Results for author:
Found 9 papers, 3 papers with code
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation
Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data.
Facial Data Minimization: Shallow Model as Your Privacy Filter
Face recognition service has been used in many fields and brings much convenience to people.
EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events
Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech's mean opinion score (MOS) without requiring the reference speech.
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)
Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion
This work explores a backdoor attack that utilizes sample-specific triggers based on voice conversion.
Semi-Supervised Semantic Segmentation with Cross Teacher Training
In experiments, the cross-teacher module significantly improves the performance of traditional student-teacher approaches, and our framework outperforms stateof-the-art methods on benchmark datasets.
Detecting and Recovering Adversarial Examples from Extracting Non-robust and Highly Predictive Adversarial Perturbations
Experimental results show that the proposed method can not only detect the adversarial examples with high accuracy, but also detect the specific category of the AEs.
Adversarial Privacy Protection on Speech Enhancement
Experimental results show that generated adversarial examples can erase most content information in original examples or replace it with target speech content through speech enhancement.
Residual-Guided Non-Intrusive Speech Quality Assessment
We generate an enhanced speech on the impaired speech to compensate for the absence of the reference audio, then pair the information of residuals with the impaired speech.
