59 citations · 84 across the 9 of their papers we have counts for
9 papers
DAFA: Distance-Aware Fair Adversarial Training
Hyungyu Lee, Saehyung Lee, Hyemi Jang +3
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aim…
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
Hyemi Jang, Junsung Park, Dahuin Jung +3
Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synth…
New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
Dahuin Jung, Dongjin Lee, Sunwon Hong +3
The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenari…
PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation
Dahuin Jung, Ho Bae, Hyun-Soo Choi +1
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego ima…
PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation
Dahuin Jung, Ho Bae, Hyun-Soo Choi +1
Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bi…
AnomiGAN: Generative adversarial networks for anonymizing private medical data
Ho Bae, Dahuin Jung, Sungroh Yoon
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence…
Security and Privacy Issues in Deep Learning
Ho Bae, Jaehee Jang, Dahuin Jung +4
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended w…
Quantized Memory-Augmented Neural Networks
Seongsik Park, Seijoon Kim, Seil Lee +2
Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural…
DNA Steganalysis Using Deep Recurrent Neural Networks
Ho Bae, Byunghan Lee, Sunyoung Kwon +1
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various me…