About

Competitions with currently unpublished results:

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Latest papers with code

An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks

29 Apr 2021anirudh9784/Adversarial-Defense

According to the recent studies, the vulnerability of state of the art Neural Networks to adversarial input samples has increased drastically.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE SELF-DRIVING CARS

0
29 Apr 2021

Fast Certified Robust Training via Better Initialization and Shorter Warmup

31 Mar 2021shizhouxing/Fast-Certified-Robust-Training

Despite state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs and are thus still quite costly for training.

ADVERSARIAL DEFENSE

5
31 Mar 2021

LiBRe: A Practical Bayesian Approach to Adversarial Detection

27 Mar 2021thudzj/ScalableBDL

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.

ADVERSARIAL DEFENSE

10
27 Mar 2021

Sandwich Batch Normalization

22 Feb 2021VITA-Group/Sandwich-Batch-Normalization

We present Sandwich Batch Normalization (SaBN), an embarrassingly easy improvement of Batch Normalization (BN) with only a few lines of code changes.

ADVERSARIAL DEFENSE CONDITIONAL IMAGE GENERATION NEURAL ARCHITECTURE SEARCH STYLE TRANSFER

32
22 Feb 2021

A Person Re-identification Data Augmentation Method with Adversarial Defense Effect

21 Jan 2021finger-monkey/Data-Augmentation

This method can not only improve the accuracy of the model, but also help the model defend against adversarial examples; 2) Multi-Modal Defense, it integrates three homogeneous modal images of visible, grayscale and sketch, and further strengthens the defense ability of the model.

ADVERSARIAL DEFENSE DATA AUGMENTATION MS-SSIM PERSON RE-IDENTIFICATION SSIM

9
21 Jan 2021

Defending against black-box adversarial attacks with gradient-free trained sign activation neural networks

1 Jan 2021zero-one-loss/scd_github

The non-transferability in our ensemble also makes it a powerful defense to substitute model black box attacks that we show require a much greater distortion than binary and full precision networks to bring our model to zero adversarial accuracy.

ADVERSARIAL DEFENSE

0
01 Jan 2021

Geometric Adversarial Attacks and Defenses on 3D Point Clouds

10 Dec 2020itailang/geometric_adv

Deep neural networks are prone to adversarial examples that maliciously alter the network's outcome.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE

14
10 Dec 2020

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

NeurIPS 2020 val-iisc/GAMA-GAT

Further, we propose Guided Adversarial Training (GAT), which achieves state-of-the-art performance amongst single-step defenses by utilizing the proposed relaxation term for both attack generation and training.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE

7
30 Nov 2020

Learnable Boundary Guided Adversarial Training

23 Nov 2020FPNAS/LBGAT

We use the model logits from one clean model $\mathcal{M}^{natural}$ to guide learning of the robust model $\mathcal{M}^{robust}$, taking into consideration that logits from the well trained clean model $\mathcal{M}^{natural}$ embed the most discriminative features of natural data, {\it e. g.}, generalizable classifier boundary.

ADVERSARIAL DEFENSE

3
23 Nov 2020

Information Obfuscation of Graph Neural Networks

28 Sep 2020liaopeiyuan/GAL

While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.

ADVERSARIAL DEFENSE GRAPH REPRESENTATION LEARNING KNOWLEDGE GRAPHS RECOMMENDATION SYSTEMS

22
28 Sep 2020