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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ICSS, IPSJ-SPT | 2024-03-22 14:55 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Adversarial Examples with Missing Perturbation Using Laser Irradiation Daisuke Kosuge, Hayato Watanabe, Taiga Manabe, Yoshihisa Takayama, Toshihiro Ohigashi (Tokai Univ.)  ICSS2023-97 |
In recent years, neural networks have made remarkable progress in the field of image processing and other areas, and the... [more] | ICSS2023-97 pp.201-207 |
| SIS | 2024-03-14 14:50 |
Kanagawa | Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Improvement of Detection Accuracy for Detection of Calcification Regions in Dental Panoramic Radiographs Using LVAT Naoki Ikeda, Sei Takano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Nanae Dewake, Nobuo Yoshinari (Matsumoto Dental Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2023-50 |
Carotid arteries on dental panoramic radiographs may show areas of calcification, a sign of vascular disease. The sudden... [more] | SIS2023-50 pp.27-32 |
| CAS, CS | 2024-03-15 14:20 |
Okinawa | Recoloring aware Countermeasure against Adversarial Examples Chisei Ishida, Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) CAS2023-134 CS2023-127 |
Adversarial Examples(AEs) which cause artificial intelligence (AI) to make a false prediction by embedded slight perturb... [more] | CAS2023-134 CS2023-127 pp.128-133 |
|
| RCC, ISEC, IT, WBS | 2024-03-13 09:15 |
Osaka | Osaka Univ. (Suita Campus) | Performance Evaluation of Visible Light Communication System Using Imaginary Images based Image Classifier Masataka Naito, Tadahiro Wada, Kaiji Mukumoto (Shizuoka Univ.), Hiraku Okada (Nagoya Univ.) IT2023-78 ISEC2023-77 WBS2023-66 RCC2023-60 |
For visible light communication systems that utilizes machine learning-based image classifiers for information embedding... [more] | IT2023-78 ISEC2023-77 WBS2023-66 RCC2023-60 pp.20-25 |
| RCC, ISEC, IT, WBS | 2024-03-14 10:20 |
Osaka | Osaka Univ. (Suita Campus) | Improving classification accuracy of imaged malware through data expansion Kaoru Yokobori, Hiroki Tanioka, Masahiko Sano, Kenji Matsuura, Tetsushi Ueta (Tokushima Univ.) IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 |
Although malware-based attacks have existed for years, malware infections increased in 2019 and 2020. One of the reaso... [more] |
IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 pp.259-264 |
| IA, SITE, IPSJ-IOT [detail] | 2024-03-13 13:40 |
Okinawa | Miyakojima City Future Creation Center (Primary: On-site, Secondary: Online) |
A Steganalysis of Image Steganography using Real Image Denoising Shinnosuke Toguchi, Takamichi Miyata (CIT) SITE2023-100 IA2023-106 |
Image steganography is a technique for embedding secret messages in images. SteganoGAN, one of the previous methods, use... [more] | SITE2023-100 IA2023-106 pp.195-202 |
| NC, MBE (Joint) |
2024-03-12 13:30 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Diffusion-Based Immediate Adversarial Purification Yuito Narisawa, Motonobu Hattori (Yamanashi Univ.) NC2023-56 |
Neural networks have achieved high performance in image classification, but there is a problem known as Adversarial Exam... [more] | NC2023-56 pp.75-80 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 09:12 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Creating Adversarial Examples to Deceive Both Humans and Machine Learning Models Ko Fujimori (Waseda Univ.), Toshiki Shibahara (NTT), Daiki Chiba (NTT Security), Mitsuaki Akiyama (NTT), Masato Uchida (Waseda Univ.) PRMU2023-65 |
One of the vulnerability attacks against neural networks is the generation of Adversarial Examples (AE), which induce mi... [more] | PRMU2023-65 pp.82-87 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 09:36 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Disabling Adversarial Examples through Color Information Processing Ryo Soeda, Masato Uchida (Waseda Univ.) PRMU2023-67 |
Image classification using neural networks is expected to have a wide range of applications, including automated driving... [more] | PRMU2023-67 pp.94-99 |
| MI | 2024-03-04 09:00 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Distance-informed adversarial learning for metal artifact reduction Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62 |
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] | MI2023-62 pp.95-98 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 10:52 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Illust Protection against Generative AI using DnCNN Yukiya Fukuda, Daiju Kanaoka (Kyutech), Hakaru Tamukoh (Kyutech/Research Center for Neuromorphic AI Hardware) PRMU2023-71 |
Although generative AI such as stable diffusion are rapidly developing, but there are concerns about problems such as un... [more] | PRMU2023-71 pp.116-121 |
| EMM | 2024-03-02 14:00 |
Overseas | Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency | [Poster Presentation]
Detection of Partial Deepfake Videos Based on Facial Feature Points Sasuke Kobayashi, Hyunho Kang (NITTC) EMM2023-91 |
(To be available after the conference date) [more] | EMM2023-91 pp.13-16 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 10:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Multi-task learning with age information model for highly accurate elderly speech recognition. Shine Takumi, Kinouchi Takahiro, Wakabayashi Yukoh, Kitaoka Norihide (TUT) EA2023-64 SIP2023-111 SP2023-46 |
The speech recognition of the elderly is less accurate, especially in smart speaker speech recognition, due to aging-rel... [more] | EA2023-64 SIP2023-111 SP2023-46 pp.19-24 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Black-Box Adversarial Attack for Math Formula Recognition Model Haruto Namura, Masatomo Yoshida (Doshisha Univ.), Nicola Adami (UNIBS), Masahiro Okuda (Doshisha Univ.) EA2023-110 SIP2023-157 SP2023-92 |
Remarkable advances in deep learning have greatly improved the accuracy of image analysis. The progress of deep learning... [more] | EA2023-110 SIP2023-157 SP2023-92 pp.289-293 |
| SeMI, IPSJ-UBI, IPSJ-MBL | 2024-02-29 15:10 |
Fukuoka | Evaluation Experiment of Display Camera Visible Light Communication Using Adversarial Examples on a Monocular Depth Estimation Model Changseok Lee, Hiraku Okada (Nagoya Univ.), Tadahiro Wada (Shizuoka Univ.), Chedlia Ben Naila, Masaaki Katayama (Nagoya Univ.) SeMI2023-75 |
Hidden display-camera visible light communication is a method of embedding data in visual information such as images and... [more] | SeMI2023-75 pp.25-30 |
|
| VLD, HWS, ICD | 2024-03-02 09:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Countermeasure on AI Hardware against Adversarial Examples Kosuke Hamaguchi, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) VLD2023-134 HWS2023-94 ICD2023-123 |
The demand for edge AI, in which artificial intelligence (AI) is directly embedded in devices, is increasing, and the se... [more] | VLD2023-134 HWS2023-94 ICD2023-123 pp.184-189 |
| ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2024-02-19 10:45 |
Hokkaido | Hokkaido Univ. | Brightness Adjustment based Countermeasure against Adversarial Examples Takumi Tojo, Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) ITS2023-47 IE2023-36 |
Recently, image classification using deep learning AI has been used for in-vehicle AI, and its accuracy and response spe... [more] | ITS2023-47 IE2023-36 pp.7-12 |
| ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2024-02-19 11:00 |
Hokkaido | Hokkaido Univ. | Improving Adversarial Robustness in Continual Learning Koki Mukai, Soichiro Kumano (UTokyo), Nicolas Michel (UGE/CNRS/LIGM), Ling Xiao, Toshihiko Yamasaki (UTokyo) ITS2023-48 IE2023-37 |
The goal of continual learning is to prevent catastrophic forgetting. However, few studies have simultaneously considere... [more] | ITS2023-48 IE2023-37 pp.13-18 |
| SAT, SANE (Joint) |
2024-02-08 13:40 |
Kagoshima | AmaHome PLAZA (Amami City Shimin Koryu Center) (Primary: On-site, Secondary: Online) |
Performance evaluation of AMD GPU and NVIDIA GPU for fitting processing of polarimetric SAR images Masato Gocho, Motofumi Arii (Mitsubishi Electric) SANE2023-106 |
We are developing the novel algorithm for the fitting of polarimetric SAR images and the general volume scattering model... [more] | SANE2023-106 pp.13-18 |
| SIP, IT, RCS | 2024-01-19 13:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures Minoru Kuribayashi (Tohoku Univ.) IT2023-67 SIP2023-100 RCS2023-242 |
It is well-known that discriminative models based on deep learning techniques may cause misclassification if adversarial... [more] | IT2023-67 SIP2023-100 RCS2023-242 p.204 |
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