Hannah Chen presented her paper on Pointwise Paraphrase Appraisal is
Potentially Problematic at the ACL 2020 Student Research
Workshop:
The prevailing approach for training and evaluating paraphrase
identification models is constructed as a binary classification
problem: the model is given a pair of sentences, and is judged by how
accurately it classifies pairs as either paraphrases or
nonparaphrases. This pointwisebased evaluation method does not match
well the objective of most real world applications, so the goal of our
work is to understand how models which perform well under pointwise
evaluation may fail in practice and find better methods for evaluating
paraphrase identification models. As a first step towards that goal,
we show that although the standard way of finetuning BERT for
paraphrase identification by pairing two sentences as one sequence
results in a model with stateoftheart performance, that model may
perform poorly on simple tasks like identifying pairs with two
identical sentences. Moreover, we show that these models may even
predict a pair of randomlyselected sentences with higher paraphrase
score than a pair of identical ones.
Paper: https://arxiv.org/abs/2005.11996
Talk Video
This blog was started in January 2008, a bit over
eight years after I started as a professor at UVA and initiated the
research group. It was named after Thomas Jefferson’s cipher wheel,
which has long
been
(and remains) one of my
favorite ways to introduce
cryptography.
Figuring out how to honor our history, including Jefferson’s founding
of the University, and appreciate his
ideals
and enormous
contributions,
while confronting the reality of Jefferson as a slave owner and
abuser, will be a challenge and responsibility for people above my
administrative rank. But, I’ve come to see that it is harmful to have
a blogged named after Jefferson so have removed the Jefferson’s Wheel
name from this research group blog.
For now, we’re going with a very generic “uvasrg” name…but hopefully
will come up with something more interesting eventually. (I’ve
reluctantly rejected “Hamilton’s Dual”, alluding, of course, to
Margaret
Hamilton
and William Rown
Hamilton, and
the Lagranian Dual and Dual
Execution, not
to any historical rival of
Jefferson’s.)
I chaired the committee to select TestofTime Awards for the IEEE Symposium on Security and Privacy symposia from 19952006, which were presented at the Opening Section of the 41^{st} IEEE Symposium on Security and Privacy.
Here's a video of Xiao Zhang's presentation at NeurIPS 2019:
https://slideslive.com/38921718/track2session1 (starting at 26:50)
See this post for info on the paper.
Here are a few pictures from NeurIPS 2019 (by Sicheng Zhu and Mohammad Mahmoody):
Finding Blackbox Adversarial Examples with Limited Queries
Blackbox attacks generate adversarial examples (AEs) against deep
neural networks with only API access to the victim model.
Existing blackbox attacks can be grouped into two main categories:

Transfer Attacks use whitebox attacks on local models to find
candidate adversarial examples that transfer to the target model.

Optimization Attacks use queries to the target model and apply
optimization techniques to search for adversarial examples.
Hybrid Attack
We propose a hybrid attack that combines transfer and optimization attacks:

Transfer Attack → Optimization Attack — take candidate adversarial examples of the local models of transfer attacks as the starting points for optimization attacks.

Optimization Attack → Transfer Attack — intermediate query results from the optimization attacks are used to finetune the local models of transfer attacks.
We validate effectiveness of the hybrid attack over the baseline on three benchmark datasets: MNIST, CIFAR10, ImageNet. In this post, we only show the results of AutoZOOM as the selected optimization method. More results of other attacks can be found in the paper.
Local Adversarial Examples are Useful (Transfer → Optimization)
Below, we compare the performance of AutoZOOM attack when it starts
from 1) the local adversarial examples, and 2) the original
points. Here, we report results for targeted attacks on normal (i.e.,
nonrobust) models:
Local AEs can substantially boost the performance of optimization
attacks, but when the same attack is used against robust
models, the improvement is small:
This ineffectiveness appears to stem from differences in the attack
space of normal and robust models. Therefore, to improve effectiveness
against robust target model, we use robust local models to produce the
transfer candidates for starting the optimization attacks. The figure
below compares impact of normal and robust local models when attacking
the robust target model:
Tuning with Byproduces Doesn’t Help Much (Optimization → Transfer)
Below, we compare the performance of AutoZOOM attack on MNIST normal
model when the local models are 1) finetuned during the attack
process, and 2) kept static:
Tuining local models using byproducts from the optimization attack
improves the query efficiency. However, for more complex datasets
(e.g., CIFAR10), we observe degradation in the attack performance by
finetuning (check Table 6 in the paper).
Batch Attacks
We consider a batch attack scenario: adversaries have limited
number of queries and want to maximize the number of adversarial
examples found within the limit. This is a more realistic way to
evaluate attacks for most adversarial purposes, then just looking at
the average cost to attack each seed in a large pool of seeds.
The number of queries required for attacking a specific seed varies
greatly across seeds:
Based on this observation, we propose twophase strategy to prioritize easy seeds for the hybrid attack:

In the first phase, the likelytotransfer seeds are prioritized
based on their PGDsteps taken to attack the local models. The
candidate adversarial example for seed seed is attempted in order to
find all the direct transfers.

In the second phase, the remaining seeds are prioritized based on
their target loss value with respect to the target model.
To validate effectievness of the twophase strategy, we compare to two seed prioritization strategies:

Retroactive Optimal: a nonrealizable attack that assumes adversaries already know the exact number of queries to attack each seed (before the attack starts) and can prioritize seeds by their actual query cost. This provides an lower bound on the query cost for an optimal strategy.

Random: this is a baseline strategy where seeds are prioritized in random order (this is the stragety assumed in most works where the adverage costs are reported).
Results for the AutoZOOM attack on a normal ImageNet model are shown below:
Our twophase strategy performs closely to the retroactive optimal
strategy and outpeforms random baseline significantly: with same
number of query limit, twophase strategy finds significantly more
adversarial examples comapred to the random baseline, and is closer to
the retroactive optimal case. (See the paper for more experimental
results and variations on the prioritization strategy.)
Main Takeaways

Transfer → Optimization: local adversarial examples can generally be used to boost optimization attacks. One caveat is, against robust target model, hybrid attack is more effective with robust local models.

Transfer → Optimization: finetuning local models is only helpful for small scale dataset (e.g., MNIST) and fails to generalize to more complex datasets. It is an open question whether we can make the finetuning process work for complex datasets.

Prioritizing seeds based on twophase strategy for the hybrid attack can significantly improve its query efficiency in batch attack scenario.
Our results make the case that it is important to evaluate both
attacks and defenses with a more realistic adversary model than just
looking at the average cost to attack a seed over a large pool of
seeds. When an adversary only need to find a small number of
adversarial examples, and has access to a large pool of potential
seeds to attack (of equal value to the adversary), then the effective
costs of a successful attack can be orders of magnitude lower than
what would be projected assuming an adversary who cannot prioritize
seeds to attack.
Paper
Fnu Suya, Jianfeng Chi, David Evans and Yuan Tian. Hybrid Batch Attacks: Finding Blackbox
Adversarial Examples with Limited Queries. In USENIX Security 2020. Boston, August 2020. [PDF] [arXiv]
Code
https://github.com/suyeecav/HybridAttack
In this repository, we provide the source code to reproduce the results in the paper. In addition, we believe our hybrid attack framework can (potentially) help boost the performance of new optimization attacks. Therefore, in the repository, we also provide tutorials to incorporate new optimization attacks into the hybrid attack framework.