Search Results for author:
Found 24 papers, 16 papers with code
Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models
In this paper, we explore how attackers can bypass these mechanisms through CEA, allowing models to circumvent watermarks even when trained on watermarked datasets.
HunyuanVideo: A Systematic Framework For Large Video Generative Models
In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models.
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
The synthesized query equipped with task-specific knowledge serves to extract the useful features for downstream tasks from the intermediate representations of the pre-trained model in a query-only manner.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i. e., achieving 95%+ attack success rate, and also being hard to be detected and removed.
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
Our methods not only provide a new perspective for designing novel attacks but also serve as a strong baseline for improving the adversarial robustness for visual grounding of MLLMs.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i. e. achieving 95%+ attack success rate and also being hard to be detected and removed.
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e. g., CLIP-ViT-B/16.
Ranked #2 on
Prompt Engineering
on ImageNet
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i. e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph.
A Dive into SAM Prior in Image Restoration
This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images.
Can SAM Boost Video Super-Resolution?
To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information.
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval
Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness.
Imperceptible and Robust Backdoor Attack in 3D Point Cloud
Existing attacks often insert some additional points into the point cloud as the trigger, or utilize a linear transformation (e. g., rotation) to construct the poisoned point cloud.
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips
The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications.
Versatile Weight Attack via Flipping Limited Bits
Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
Improving Vision Transformers by Revisiting High-frequency Components
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on
Domain Generalization
on Stylized-ImageNet
Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning
To this end, we propose the confusing perturbations-induced backdoor attack (CIBA).
Universal Adversarial Head: Practical Protection against Video Data Leakage
We propose $universal \ adversarial \ head$ (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space.
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
Open-sourced Dataset Protection via Backdoor Watermarking
Based on the proposed backdoor-based watermarking, we use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model of the benign samples and their correspondingly watermarked samples ($i. e.$, images with trigger) on the target class.
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
Targeted Attack for Deep Hashing based Retrieval
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
How to obtain a model with good interpretability and performance has always been an important research topic.
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.
