10 research outputs found
Label Poisoning is All You Need
In a backdoor attack, an adversary injects corrupted data into a model's
training dataset in order to gain control over its predictions on images with a
specific attacker-defined trigger. A typical corrupted training example
requires altering both the image, by applying the trigger, and the label.
Models trained on clean images, therefore, were considered safe from backdoor
attacks. However, in some common machine learning scenarios, the training
labels are provided by potentially malicious third-parties. This includes
crowd-sourced annotation and knowledge distillation. We, hence, investigate a
fundamental question: can we launch a successful backdoor attack by only
corrupting labels? We introduce a novel approach to design label-only backdoor
attacks, which we call FLIP, and demonstrate its strengths on three datasets
(CIFAR-10, CIFAR-100, and Tiny-ImageNet) and four architectures (ResNet-32,
ResNet-18, VGG-19, and Vision Transformer). With only 2% of CIFAR-10 labels
corrupted, FLIP achieves a near-perfect attack success rate of 99.4% while
suffering only a 1.8% drop in the clean test accuracy. Our approach builds upon
the recent advances in trajectory matching, originally introduced for dataset
distillation
Scalable Extraction of Training Data from (Production) Language Models
This paper studies extractable memorization: training data that an adversary
can efficiently extract by querying a machine learning model without prior
knowledge of the training dataset. We show an adversary can extract gigabytes
of training data from open-source language models like Pythia or GPT-Neo,
semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing
techniques from the literature suffice to attack unaligned models; in order to
attack the aligned ChatGPT, we develop a new divergence attack that causes the
model to diverge from its chatbot-style generations and emit training data at a
rate 150x higher than when behaving properly. Our methods show practical
attacks can recover far more data than previously thought, and reveal that
current alignment techniques do not eliminate memorization
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as
Stable Diffusion and GPT-4, yet their design does not receive the same research
attention as model architectures or training algorithms. To address this
shortcoming in the ML ecosystem, we introduce DataComp, a testbed for dataset
experiments centered around a new candidate pool of 12.8 billion image-text
pairs from Common Crawl. Participants in our benchmark design new filtering
techniques or curate new data sources and then evaluate their new dataset by
running our standardized CLIP training code and testing the resulting model on
38 downstream test sets. Our benchmark consists of multiple compute scales
spanning four orders of magnitude, which enables the study of scaling trends
and makes the benchmark accessible to researchers with varying resources. Our
baseline experiments show that the DataComp workflow leads to better training
sets. In particular, our best baseline, DataComp-1B, enables training a CLIP
ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming
OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training
procedure and compute. We release DataComp and all accompanying code at
www.datacomp.ai
Age-related changes to motor synergies in multi-joint and multi-finger manipulative skills: a meta-analysis
Purpose The aim of the current meta-analysis was to examine the extent to which there are differences in upper extremity
motor synergies across different age groups in manipulative tasks.
Methods The studies that used the uncontrolled manifold method to examine the effect of age on motor synergies in multijoint
and multi-finger tasks were selected. Sixteen relevant studies from 1154 articles were selected for the meta-analysis—4
and 12 studies considered multi-joint kinematics and multi-finger kinetic tasks respectively.
Results The results of the meta-analysis suggested reduced strength of synergies in multi-finger task in older adults, but
this was not the case for synergies in multi-joint task. Part of this age-related difference in finger function is related to the
increased variability in total force in grasping tasks. However, reductions in the strength of multi-finger synergies in hand
functions following ageing appear to depend on the characteristics of the task.
Conclusions These findings indicate that the cooperation among fingers to stabilise the total required force to apply for
grasping and other fine motor skills is less efficient in older adults that might affect the quality of manipulative tasks
Glutaraldehyde is an effective cross-linker for production of antibodies against advanced glycation end-products
Towards a Defense against Backdoor Attacks in Continual Federated Learning
Backdoor attacks are a major concern in federated learning (FL) pipelines
where training data is sourced from untrusted clients over long periods of time
(i.e., continual learning). Preventing such attacks is difficult because
defenders in FL do not have access to raw training data. Moreover, in a
phenomenon we call backdoor leakage, models trained continuously eventually
suffer from backdoors due to cumulative errors in backdoor defense mechanisms.
We propose a novel framework for defending against backdoor attacks in the
federated continual learning setting. Our framework trains two models in
parallel: a backbone model and a shadow model. The backbone is trained without
any defense mechanism to obtain good performance on the main task. The shadow
model combines recent ideas from robust covariance estimation-based filters
with early-stopping to control the attack success rate even as the data
distribution changes. We provide theoretical motivation for this design and
show experimentally that our framework significantly improves upon existing
defenses against backdoor attacks