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Found 15 papers, 1 papers with code
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation.
BadEdit: Backdooring large language models by model editing
It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One
Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions.
Purifying Large Language Models by Ensembling a Small Language Model
The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources.
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution
To expose this potential vulnerability, we aim to build an adversarial attack forcing SDEdit to generate a specific data distribution aligned with a specified attribute (e. g., female), without changing the input's attribute characteristics.
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving.
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
The underlying rationale behind our idea is that image resampling can alleviate the influence of adversarial perturbations while preserving essential semantic information, thereby conferring an inherent advantage in defending against adversarial attacks.
FAIRER: Fairness as Decision Rationale Alignment
Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment.
On the Robustness of Segment Anything
Second, we extend representative adversarial attacks against SAM and study the influence of different prompts on robustness.
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Machine learning (ML) systems have achieved remarkable performance across a wide area of applications.
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models
Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for high accessible backdoor attacks.
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs.
Unveiling Project-Specific Bias in Neural Code Models
We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness.
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
In this paper, we first draw the close connection between adversarial robustness and neuron sensitivities, as sensitive neurons make the most non-trivial contributions to model predictions in the adversarial setting.
Knowledge Consistency between Neural Networks and Beyond
As a generic tool, our method can be broadly used for different applications.
