1 code implementation • 14 Jan 2025 • Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza, Olivier Déforges, Kassem Kallas
This demonstrates the vulnerability of DNNs to energy backdoor attacks.
1 code implementation • 18 Mar 2024 • Théophile Blard, Théo Ladune, Pierrick Philippe, Gordon Clare, Xiaoran Jiang, Olivier Déforges
Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel.
1 code implementation • 5 Feb 2024 • Thomas Leguay, Théo Ladune, Pierrick Philippe, Olivier Déforges
We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall.
no code implementations • 9 Oct 2023 • Kevin Reuzé, Wassim Hamidouche, Pierrick Philippe, Olivier Déforges
The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential for efficient compression of Intra coded blocks.
no code implementations • 19 Jul 2023 • Xiaohong Liu, Xiongkuo Min, Wei Sun, Yulun Zhang, Kai Zhang, Radu Timofte, Guangtao Zhai, Yixuan Gao, Yuqin Cao, Tengchuan Kou, Yunlong Dong, Ziheng Jia, Yilin Li, Wei Wu, Shuming Hu, Sibin Deng, Pengxiang Xiao, Ying Chen, Kai Li, Kai Zhao, Kun Yuan, Ming Sun, Heng Cong, Hao Wang, Lingzhi Fu, Yusheng Zhang, Rongyu Zhang, Hang Shi, Qihang Xu, Longan Xiao, Zhiliang Ma, Mirko Agarla, Luigi Celona, Claudio Rota, Raimondo Schettini, Zhiwei Huang, Yanan Li, Xiaotao Wang, Lei Lei, Hongye Liu, Wei Hong, Ironhead Chuang, Allen Lin, Drake Guan, Iris Chen, Kae Lou, Willy Huang, Yachun Tasi, Yvonne Kao, Haotian Fan, Fangyuan Kong, Shiqi Zhou, Hao liu, Yu Lai, Shanshan Chen, Wenqi Wang, HaoNing Wu, Chaofeng Chen, Chunzheng Zhu, Zekun Guo, Shiling Zhao, Haibing Yin, Hongkui Wang, Hanene Brachemi Meftah, Sid Ahmed Fezza, Wassim Hamidouche, Olivier Déforges, Tengfei Shi, Azadeh Mansouri, Hossein Motamednia, Amir Hossein Bakhtiari, Ahmad Mahmoudi Aznaveh
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
no code implementations • 5 Jun 2022 • Ahmed Aldahdooh, Wassim Hamidouche, Olivier Déforges
Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studied for convolutional neural network (CNN).
no code implementations • 24 Jul 2021 • Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
The proposed module has been integrated in an ASIC UHD decoder targeting energy-aware decoding of VVC videos on consumer devices.
1 code implementation • 19 Apr 2021 • Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
This paper introduces a practical learned video codec.
no code implementations • ICLR Workshop Neural_Compression 2021 • Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
This paper introduces a novel framework for end-to-end learned video coding.
1 code implementation • 9 Mar 2021 • Ahmed Aldahdooh, Wassim Hamidouche, Olivier Déforges
Moreover, the state-of-the-art detection techniques have been designed for specific attacks or broken by others, need knowledge about the attacks, are not consistent, increase model parameters overhead, are time-consuming, or have latency in inference time.
no code implementations • 6 Aug 2020 • Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection.
no code implementations • 6 Jul 2020 • Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression.
1 code implementation • 1 Jun 2019 • Sid Ahmed Fezza, Yassine Bakhti, Wassim Hamidouche, Olivier Déforges
However, all the works proposed in the literature for generating adversarial examples have used the $L_{p}$ norms ($L_{0}$, $L_{2}$ and $L_{\infty}$) as distance metrics to quantify the similarity between the original image and the adversarial example.