COINW列出了基于Solana的Meme Coin RFC（延迟查找器硬币），打开RFC/USDT交易

RFC是由Twitter帐户@ifindredards发行的Solana区块链建立的模因令牌。以其讽刺评论和参与而闻名

CoinW, a renowned crypto trading platform, has announced the listing of Retard Finder Coin (RFC), a Solana-based meme coin known for its satirical Twitter commentary and community engagement. The exchange will commence trading of the RFC/USDT pair at 1:00 pm (UTC+8) on April 9th. To celebrate the listing of RFC, CoinW is hosting the “RFC Bounty Program” event with a reward pool of $5,000 USDT.

Coinw是一个著名的加密货币交易平台，宣布宣布将基于索拉纳的Meme Coin列出了雷德发现者硬币（RFC），以其讽刺的Twitter评论和社区参与而闻名。交易所将于4月9日下午1:00（UTC+8）开始交易RFC/USDT对。为了庆祝RFC的上市，Coinw将举办“ RFC赏金计划”活动，挂池为5,000美元。

Beginning at 5:00 (UTC) on April 9th and concluding at 16:00 (UTC) on April 16th, members of the CoinW community can participate in various events to win a share of the 5,000 USDT prize pool. Registration on the CoinW platform, trading the newly listed RFC/USDT pair, and engaging in community events on Telegram and Twitter will contribute to earning rewards.

从4月9日的5:00（UTC）开始，并于4月16日在16:00（UTC）结束时，Coinw社区的成员可以参加各种活动，以赢得5,000 USDT奖励池中的一部分。在Coinw平台上进行注册，交易新列出的RFC/USDT对，并在Telegram和Twitter上参加社区活动会有助于赚取奖励。

Created by the popular Twitter account @ifindretards, which boasts over 700,000 followers known for its satirical commentary and engagement with a vast community of followers, including frequent interactions with well-known Twitter celebrities. The account has gained immense attention for its humorous takes on cryptocurrency and online culture.

由受欢迎的Twitter帐户@IfindReDards创建，该帐户拥有700,000多名追随者，以其讽刺的评论和与众多追随者社区的交往，包括与知名Twitter名人的频繁互动。该帐户因其对加密货币和在线文化的幽默感而引起了极大的关注。

According to CoinW Research, RFC is a community-driven meme coin without functional utility but has attracted significant attention due to its unique social media narrative. It follows a fair launch model, with 96% of the total supply distributed to the community and only 4% allocated to the developer wallet for liquidity.

根据Coinw Research的说法，RFC是一个没有功能效用的社区驱动的模因硬币，但由于其独特的社交媒体叙事而引起了极大的关注。它遵循公平的发布模型，占社区总供应量的96％，只有4％分配给开发人员钱包以获得流动性。

After a successful community vote on April 1st, 2025, to determine the best time for listing, CoinW will be introducing the listing of Retard Finder Coin (RFC).

在2025年4月1日成功进行了社区投票以确定上市的最佳时间之后，Coinw将引入Readard Finder Coin（RFC）的清单。

Applied Sciences Department, Faculty of Science, University of Technology, Malaysia

马来西亚科技大学科学学院应用科学系

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In this paper, we propose a novel approach for blind image watermarking using a hybrid deep learning architecture. The proposed method combines the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to embed and extract watermarks in a robust and efficient manner. CNNs are used to extract spatial and spectral features from the cover image, while RNNs are used to model the temporal dependencies among the extracted features. The watermark is then embedded into the cover image using a specially designed embedding module, which minimizes the perceptual distortion of the stamped image. To extract the watermark, a decoder network is designed to recover the watermark bits from the stamped image. Experimental results demonstrate that the proposed method outperforms existing methods in terms of both robustness and imperceptibility. The method is robust to common image processing attacks, such as Gaussian noise, JPEG compression, and scaling. Moreover, the proposed method can achieve high imperceptibility, rendering the embedded watermark invisible to the naked eye.

在本文中，我们提出了一种使用混合深度学习体系结构的新型方法，用于盲图水印。提出的方法结合了卷积神经网络（CNN）和复发性神经网络（RNN）的优势，以稳健有效的方式嵌入和提取水印。 CNN用于从封面图像中提取空间和光谱特征，而RNN用于对提取特征之间的时间依赖性进行建模。然后，使用特殊设计的嵌入模块将水印嵌入封面图像中，从而最大程度地减少了盖章图像的感知失真。为了提取水印，设计一个解码器网络旨在从盖章图像中恢复水印位。实验结果表明，所提出的方法在鲁棒性和不可识别方面都优于现有方法。该方法对常见的图像处理攻击具有鲁棒性，例如高斯噪声，JPEG压缩和缩放。此外，所提出的方法可以实现高度易于识别，从而使嵌入式水印对肉眼看不见。

This paper proposes a novel approach for blind image watermarking using a hybrid deep learning architecture. The proposed method combines the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to embed and extract watermarks in a robust and efficient manner. CNNs are used to extract spatial and spectral features from the cover image, while RNNs are used to model the temporal dependencies among the extracted features. The watermark is then embedded into the cover image using a specially designed embedding module, which minimizes the perceptual distortion of the stamped image. To extract the watermark, a decoder network is designed to recover the watermark bits from the stamped image. Experimental results demonstrate that the proposed method outperforms existing methods in terms of both robustness and imperceptibility. The method is robust to common image processing attacks, such as Gaussian noise, JPEG compression, and scaling. Moreover, the proposed method can achieve high imperceptibility, rendering the embedded watermark invisible to the naked eye.

本文提出了一种使用混合深度学习结构的新型方法，用于盲图水印。提出的方法结合了卷积神经网络（CNN）和复发性神经网络（RNN）的优势，以稳健有效的方式嵌入和提取水印。 CNN用于从封面图像中提取空间和光谱特征，而RNN用于对提取特征之间的时间依赖性进行建模。然后，使用特殊设计的嵌入模块将水印嵌入封面图像中，从而最大程度地减少了盖章图像的感知失真。为了提取水印，设计一个解码器网络旨在从盖章图像中恢复水印位。实验结果表明，所提出的方法在鲁棒性和不可识别方面都优于现有方法。该方法对常见的图像处理攻击具有鲁棒性，例如高斯噪声，JPEG压缩和缩放。此外，所提出的方法可以实现高度易于识别，从而使嵌入式水印对肉眼看不见。

Image watermarking is an important technique for protecting digital content. It involves embedding a watermark signal into a cover image to identify the copyright holder or track the usage of the image. The watermark should be robust to common image processing attacks, such as Gaussian noise, JPEG compression, and scaling. At the same time, the watermark should be imperceptible to avoid affecting the visual quality of the image.

图像水印是保护数字内容的重要技术。它涉及将水印信号嵌入封面图像中，以识别版权持有人或跟踪图像的使用情况。水印应适用于常见的图像处理攻击，例如高斯噪声，JPEG压缩和缩放。同时，应避免影响图像的视觉质量的水印。

Deep learning has achieved promising results in various low-level vision tasks, such as image denoising, super-resolution, and image manipulation detection. Recently, deep learning methods have also been applied to image watermarking. Convolutional neural networks (CNNs) are good at extracting spatial and spectral features from images, while recurrent neural networks (RNNs) are suitable for modeling temporal dependencies among data.

深度学习在各种低级视觉任务中取得了令人鼓舞的结果，例如图像降解，超分辨率和图像操纵检测。最近，深度学习方法也已应用于图像水印。卷积神经网络（CNN）擅长从图像中提取空间和光谱特征，而复发性神经网络（RNN）适合在数据之间建模时间依赖性。

In this paper, we propose a hybrid deep learning architecture for blind image watermarking, which combines the strengths of CNNs and RNNs. CNNs are used to extract features from the cover image, and RNNs are used to embed the watermark into the extracted features. A specially designed embedding module is proposed to minimize the perceptual distortion of the stamped image. To extract the

在本文中，我们提出了用于盲图水印的混合深度学习结构，结合了CNN和RNN的优势。 CNN用于从封面图像中提取特征，RNN用于将水印嵌入到提取的特征中。提出了一个专门设计的嵌入模块，以最大程度地减少邮票图像的感知失真。提取