1 code implementation • COLING 2022 • Ge Luo, Hebi Li, Youbiao He, Forrest Sheng Bao
Evaluating machine-generated summaries without a human-written reference summary has been a need for a long time.
no code implementations • 22 Nov 2023 • Ge Luo, Junqiang Huang, Manman Zhang, Zhenxing Qian, Sheng Li, Xinpeng Zhang
In various fine-tune scenarios and against watermark attack methods, our research confirms that analyzing the distribution of watermarks in artificially generated images reliably detects unauthorized mimicry.
no code implementations • 10 May 2023 • Ping Wei, Ge Luo, Qi Song, Xinpeng Zhang, Zhenxing Qian, Sheng Li
In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images.
1 code implementation • 20 Dec 2022 • Forrest Sheng Bao, Ruixuan Tu, Ge Luo, Yinfei Yang, Hebi Li, Minghui Qiu, Youbiao He, Cen Chen
Automated summary quality assessment falls into two categories: reference-based and reference-free.
no code implementations • 28 Jul 2022 • Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo, Zhenxing Qian, Qing Zhou
A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media.
1 code implementation • NAACL 2022 • Forrest Sheng Bao, Hebi Li, Ge Luo, Minghui Qiu, Yinfei Yang, Youbiao He, Cen Chen
Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain.