The increasing prominence of deep learning applications and reliance on
personalized data underscore the urgent need to address privacy
vulnerabilities, particularly Membership Inference Attacks (MIAs). Despite
numerous MIA studies, significant knowledge gaps persist, particularly
regarding the impact of hidden features (in isolation) on attack efficacy and
insufficient justification for the root causes of attacks based on raw data
features. In this paper, we aim to address these knowledge gaps by first
exploring statistical approaches to identify the most informative neurons and
quantifying the significance of the hidden activations from the selected
neurons on attack accuracy, in isolation and combination. Additionally, we
propose an attack-driven explainable framework by integrating the target and
attack models to identify the most influential features of raw data that lead
to successful membership inference attacks. Our proposed MIA shows an
improvement of up to 26% on state-of-the-art MIA.

通过统计方法，本文旨在解决隐含特征对攻击效果的影响以及基于原始数据特征的攻击根本原因不足的问题，并提出了一个攻击驱动的可解释框架，通过集成目标和攻击模型，识别导致成功成员推理攻击的原始数据上最具影响力的特征。我们提出的攻击显示了与最先进的攻击技术相比高达 26％的改进。

揭示无法察觉的事物：通过可解释性探索白盒成员推断的视角

Unveiling the Unseen: Exploring Whitebox Membership Inference through  the Lens of Explainability

Large language models (LLM) have emerged as a powerful tool for AI, with the
key ability of in-context learning (ICL), where they can perform well on unseen
tasks based on a brief series of task examples without necessitating any
adjustments to the model parameters. One recent interesting mysterious
observation is that models of different scales may have different ICL
behaviors: larger models tend to be more sensitive to noise in the test
context. This work studies this observation theoretically aiming to improve the
understanding of LLM and ICL. We analyze two stylized settings: (1) linear
regression with one-layer single-head linear transformers and (2) parity
classification with two-layer multiple attention heads transformers (non-linear
data and non-linear model). In both settings, we give closed-form optimal
solutions and find that smaller models emphasize important hidden features
while larger ones cover more hidden features; thus, smaller models are more
robust to noise while larger ones are more easily distracted, leading to
different ICL behaviors. This sheds light on where transformers pay attention
to and how that affects ICL. Preliminary experimental results on large base and
chat models provide positive support for our analysis.

大型语言模型（LLM）通过上下文学习（ICL）的关键能力成为 AI 的强大工具，本研究探讨了不同规模的模型在 ILC 行为上的不同性质，并在两个设定下分析了变压器的注意力机制与 ICL 的关系。

为什么更大的语言模型在上下文中学习方式不同？

Why Larger Language Models Do In-context Learning Differently?

Sign Language Production (SLP) is the tough task of turning sign language
into sign videos. The main goal of SLP is to create these videos using a sign
gloss. In this research, we've developed a new method to make high-quality sign
videos without using human poses as a middle step. Our model works in two main
parts: first, it learns from a generator and the video's hidden features, and
next, it uses another model to understand the order of these hidden features.
To make this method even better for sign videos, we make several significant
improvements. (i) In the first stage, we take an improved 3D VQ-GAN to learn
downsampled latent representations. (ii) In the second stage, we introduce
sequence-to-sequence attention to better leverage conditional information.
(iii) The separated two-stage training discards the realistic visual semantic
of the latent codes in the second stage. To endow the latent sequences semantic
information, we extend the token-level autoregressive latent codes learning
with perceptual loss and reconstruction loss for the prior model with visual
perception. Compared with previous state-of-the-art approaches, our model
performs consistently better on two word-level sign language datasets, i.e.,
WLASL and NMFs-CSL.

经过改进，我们开发了一种新方法来制作高质量的手语视频，不需要使用人类姿势作为中间步骤。与之前的方法相比，我们的模型在两个手语数据集上表现更好。