Youn Kyu Lee

Hyun Jun Yook, Ga San Jhun, Jae Hyun Cho et al.

Machine unlearning (MU) removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can exploit unlearned models to generate content containing removed concepts, posing a significant security risk. However, existing adversarial attack methods still face challenges in generating content that aligns with an attacker's intent while incurring high computational costs to identify successful prompts. To address these challenges, we propose ZIUM, a Zero-shot Intent-aware adversarial attack on Unlearned Models, which enables the flexible customization of target attack images to reflect an attacker's intent. Additionally, ZIUM supports zero-shot adversarial attacks without requiring further optimization for previously attacked unlearned concepts. The evaluation across various MU scenarios demonstrated ZIUM's effectiveness in successfully customizing content based on user-intent prompts while achieving a superior attack success rate compared to existing methods. Moreover, its zero-shot adversarial attack significantly reduces the attack time for previously attacked unlearned concepts.

Youngkee Kim, Won Joon Yun, Youn Kyu Lee et al.

In many deep neural network (DNN) applications, the difficulty of gathering high-quality data in the industry field hinders the practical use of DNN. Thus, the concept of transfer learning has emerged, which leverages the pretrained knowledge of DNNs trained on large-scale datasets. Therefore, this paper suggests two-stage architectural fine-tuning, inspired by neural architecture search (NAS). One of main ideas is mutation, which reduces the search cost using given architectural information. Moreover, early-stopping is considered which cuts NAS costs by terminating the search process in advance. Experimental results verify our proposed method reduces 32.4% computational and 22.3% searching costs.

Youngkee Kim, Won Joon Yun, Youn Kyu Lee et al.

In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications. In this paper, the main research trends of neural architecture search (NAS) are classified as neuro-evolutionary algorithms, reinforcement learning based algorithms, and one-shot architecture search approaches. Furthermore, each research trend is introduced and finally all the major three trends are compared. Lastly, the future research directions of NAS research trends are discussed.

Arman Shahbazian, Youn Kyu Lee, Duc Le et al.

Over the past three decades, considerable effort has been devoted to the study of software architecture. A major portion of this effort has focused on the originally proposed view of four "C"s---components, connectors, configurations, and constraints---that are the building blocks of a system's architecture. Despite being simple and appealing, this view has proven to be incomplete and has required further elaboration. To that end, researchers have more recently tried to approach architectures from another important perspective---that of design decisions that yield a system's architecture. These more recent efforts have lacked a precise understanding of several key questions, however: (1) What is an architectural design decision (definition)? (2) How can architectural design decisions be found in existing systems (identification)? (3) What system decisions are and are not architectural (classification)? (4) How are architectural design decisions manifested in the code (reification)? (5) How can important architectural decisions be preserved and/or changed as desired (evolution)? This paper presents a technique targeted at answering these questions by analyzing information that is readily available about software systems. We applied our technique on over 100 different versions of two widely adopted open- source systems, and found that it can accurately uncover the architectural design decisions embodied in the systems.