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Found 7 papers, 6 papers with code
Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors
The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3.
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP).
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model.
CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction
Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology.
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
DP-Net: Dynamic Programming Guided Deep Neural Network Compression
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs).
Pose2Seg: Detection Free Human Instance Segmentation
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Ranked #1 on
Human Instance Segmentation
on OCHuman
