Search Results for author:
Found 56 papers, 27 papers with code
Sociolectal Analysis of Pretrained Language Models
Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks.
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting
Based on our analysis, we provide a simple yet effective way to boost the adversarial robustness of MAE.
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation.
GPT Paternity Test: GPT Generated Text Detection with GPT Genetic Inheritance
By comparing the similarity between the original text and the generated re-answered text, it can be determined whether the text is machine-generated.
Watermarking Text Generated by Black-Box Language Models
To allow third-parties to autonomously inject watermarks into generated text, we develop a watermarking framework for black-box language model usage scenarios.
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 10% labeled data
(detector metric)
Diversity-Aware Meta Visual Prompting
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.
Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network
At first, a top-n selection strategy is proposed to provide pseudo-labels for public data, and use pseudo-labels to guide the training of the cGAN.
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Being capable of capturing information in challenging visual conditions, event cameras have the potential to overcome the limitations of frame-based cameras in the computer vision and robotics community.
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory.
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable.
Ranked #5 on
Instance Segmentation
on LVIS v1.0 val
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion
This approach enables effective defense against adaptive attacks with varying noise budgets, achieving accentuated robustness of existing 3D deep recognition models.
PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition
Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations.
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language.
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance.
Invertible Mask Network for Face Privacy-Preserving
Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.
Shape-invariant 3D Adversarial Point Clouds
In this paper, we propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.
Self-supervised Transformer for Deepfake Detection
After pre-training with our method, the model will then be partially fine-tuned for deepfake detection task.
Protecting Celebrities from DeepFake with Identity Consistency Transformer
In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions.
Tracing Text Provenance via Context-Aware Lexical Substitution
Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news.
HairCLIP: Design Your Hair by Text and Reference Image
Hair editing is an interesting and challenging problem in computer vision and graphics.
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
This paper explores a better prediction target for BERT pre-training of vision transformers.
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition
As an effective method for intellectual property (IP) protection, model watermarking technology has been applied on a wide variety of deep neural networks (DNN), including speech classification models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression
To address such limitations, we proposed a novel end-to-end training architecture, which utilizes Mini-Batch of Real and Simulated JPEG compression (MBRS) to enhance the JPEG robustness.
Poison Ink: Robust and Invisible Backdoor Attack
As the image structure can keep its semantic meaning during the data transformation, such trigger pattern is inherently robust to data transformations.
Exploring Structure Consistency for Deep Model Watermarking
However, little attention has been devoted to the protection of DNNs in image processing tasks.
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
By further pretraining on the larger dataset ImageNet-21K, we achieve 87. 5% Top-1 accuracy on ImageNet-1K and high segmentation performance on ADE20K with 55. 7 mIoU.
Ranked #25 on
Semantic Segmentation
on ADE20K val
E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion
This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks.
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication
Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift.
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
Deep Model Intellectual Property Protection via Deep Watermarking
By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model.
Multi-attentional Deepfake Detection
Most of them model deepfake detection as a vanilla binary classification problem, i. e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake).
A Brief Survey on Deep Learning Based Data Hiding
Data hiding is the art of concealing messages with limited perceptual changes.
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns.
Adversarial Examples Detection beyond Image Space
To detect both few-perturbation attacks and large-perturbation attacks, we propose a method beyond image space by a two-stream architecture, in which the image stream focuses on the pixel artifacts and the gradient stream copes with the confidence artifacts.
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation
Image matting is a fundamental and challenging problem in computer vision and graphics.
Identity-Driven DeepFake Detection
Our approach takes as input the suspect image/video as well as the target identity information (a reference image or video).
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack.
Passport-aware Normalization for Deep Model Protection
Only when the model IP is suspected to be stolen by someone, the private passport-aware branch is added back for ownership verification.
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels.
Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer
Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks.
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deepfake defense not only requires the research of detection but also requires the efforts of generation methods.
Ranked #1 on
Face Swapping
on FaceForensics++
Model Watermarking for Image Processing Networks
In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward.
Self-supervised Adversarial Training
Recent work has demonstrated that neural networks are vulnerable to adversarial examples.
Reversible Adversarial Attack based on Reversible Image Transformation
In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise.
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.
Neural Machine Reading Comprehension: Methods and Trends
Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
We propose a Denoiser and UPsampler Network (DUP-Net) structure as defenses for 3D adversarial point cloud classification, where the two modules reconstruct surface smoothness by dropping or adding points.
Emerging Applications of Reversible Data Hiding
Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence without loss.
CAAD 2018: Iterative Ensemble Adversarial Attack
Deep Neural Networks (DNNs) have recently led to significant improvements in many fields.
CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network
In this paper, we propose an improvement of Adversarial Transformation Networks(ATN) to generate adversarial examples, which can fool white-box models and black-box models with a state of the art performance and won the 2rd place in the non-target task in CAAD 2018.
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples.
Detection based Defense against Adversarial Examples from the Steganalysis Point of View
Moreover, secondary adversarial attacks cannot be directly performed to our method because our method is not based on a neural network but based on high-dimensional artificial features and FLD (Fisher Linear Discriminant) ensemble.
Enhanced Attacks on Defensively Distilled Deep Neural Networks
In this paper, we first propose the epsilon-neighborhood attack, which can fool the defensively distilled networks with 100% success rate in the white-box setting, and it is fast to generate adversarial examples with good visual quality.
