Recent deep-learning-based compression methods have achieved superior
performance compared with traditional approaches. However, deep learning models
have proven to be vulnerable to backdoor attacks, where some specific trigger
patterns added to the input can lead to malicious behavior of the models. In
this paper, we present a novel backdoor attack with multiple triggers against
learned image compression models. Motivated by the widely used discrete cosine
transform (DCT) in existing compression systems and standards, we propose a
frequency-based trigger injection model that adds triggers in the DCT domain.
In particular, we design several attack objectives for various attacking
scenarios, including: 1) attacking compression quality in terms of bit-rate and
reconstruction quality; 2) attacking task-driven measures, such as down-stream
face recognition and semantic segmentation. Moreover, a novel simple dynamic
loss is designed to balance the influence of different loss terms adaptively,
which helps achieve more efficient training. Extensive experiments show that
with our trained trigger injection models and simple modification of encoder
parameters (of the compression model), the proposed attack can successfully
inject several backdoors with corresponding triggers in a single image
compression model.

本文提出了一种基于频率的触发器注入模型，针对训练好的图像压缩模型进行多触发器的后门攻击，攻击目标包括压缩质量和任务驱动的度量，并设计了一种简单的动态损失函数以平衡不同损失项的影响，实验结果表明我们的提出的攻击方法能够成功地在单个图像压缩模型中注入多个后门触发器。

自适应频率触发的深度图像压缩后门攻击

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency  Trigger

Compressing word embeddings is important for deploying NLP models in
memory-constrained settings. However, understanding what makes compressed
embeddings perform well on downstream tasks is challenging---existing measures
of compression quality often fail to distinguish between embeddings that
perform well and those that do not. We thus propose the eigenspace overlap
score as a new measure. We relate the eigenspace overlap score to downstream
performance by developing generalization bounds for the compressed embeddings
in terms of this score, in the context of linear and logistic regression. We
then show that we can lower bound the eigenspace overlap score for a simple
uniform quantization compression method, helping to explain the strong
empirical performance of this method. Finally, we show that by using the
eigenspace overlap score as a selection criterion between embeddings drawn from
a representative set we compressed, we can efficiently identify the better
performing embedding with up to $2\times$ lower selection error rates than the
next best measure of compression quality, and avoid the cost of training a
model for each task of interest.

该研究提出了基于特征空间重叠度的嵌入压缩质量度量方法，并探究了压缩后词嵌入对线性和逻辑回归下游任务的影响，同时证明了基于均匀量化的压缩方法的高效性和实用性。