Minwoo Jeon

Gahee Kim, Minwoo Jeon, Hyun Do Choi et al.

In this study, a novel active solubility sensing device using computer vision is proposed to improve separation purification performance and prevent malfunctions of separation equipment such as preparative liquid chromatographers and evaporators. The proposed device actively measures the solubility by transmitting a solution using a background image. The proposed system is a combination of a device that uses a background image and a method for estimating the dissolution and particle presence by changing the background image. The proposed device consists of four parts: camera, display, adjustment, and server units. The camera unit is made up of a rear image sensor on a mobile phone. The display unit is comprised of a tablet screen. The adjustment unit is composed of rotating and height-adjustment jigs. Finally, the server unit consists of a socket server for communication between the units and a PC, including an automated solubility analysis system implemented in Python. The dissolution status of the solution was divided into four categories and a case study was conducted. The algorithms were trained based on these results. Six organic materials and four organic solvents were combined with 202 tests to train the developed algorithm. As a result, the evaluation rate for the dissolution state exhibited an accuracy of 95 %. In addition, the device and method must develop a feedback function that can add a solvent or solute after dissolution detection using solubility results for use in autonomous systems, such as a synthetic automation system. Finally, the diversification of the sensing method is expected to extend not only to the solution but also to the solubility and homogeneity analysis of the film.

Geunhyeok Yu, Minwoo Jeon, Hyoseok Hwang

The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we propose Decision-BADGE, a novel method to craft universal adversarial perturbations for executing decision-based black-box attacks. To optimize perturbation with decisions, we addressed two challenges, namely the magnitude and the direction of the gradient. First, we use batch loss, differences from distributions of ground truth, and accumulating decisions in batches to determine the magnitude of the gradient. This magnitude is applied in the direction of the revised simultaneous perturbation stochastic approximation (SPSA) to update the perturbation. This simple yet efficient method can be easily extended to score-based attacks as well as targeted attacks. Experimental validation across multiple victim models demonstrates that the Decision-BADGE outperforms existing attack methods, even image-specific and score-based attacks. In particular, our proposed method shows a superior success rate with less training time. The research also shows that Decision-BADGE can successfully deceive unseen victim models and accurately target specific classes.

Tae-Young Lee, Sundong Park, Minwoo Jeon et al.

As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their personal knowledge in trained models. Despite several research efforts to address this, they often fail to consider the real-world demand from users for complete knowledge erasure. Furthermore, our investigation reveals that existing methods have a risk of leaking personal knowledge through embedding features. To address these issues, we introduce a novel concept of Knowledge Deletion (KD), an advanced task that considers both concerns, and provides an appropriate metric, named Knowledge Retention score (KR), for assessing knowledge retention in feature space. To achieve this, we propose a novel training-free erasing approach named Erasing Space Concept (ESC), which restricts the important subspace for the forgetting knowledge by eliminating the relevant activations in the feature. In addition, we suggest ESC with Training (ESC-T), which uses a learnable mask to better balance the trade-off between forgetting and preserving knowledge in KD. Our extensive experiments on various datasets and models demonstrate that our proposed methods achieve the fastest and state-of-the-art performance. Notably, our methods are applicable to diverse forgetting scenarios, such as facial domain setting, demonstrating the generalizability of our methods. The code is available at http://github.com/KU-VGI/ESC .