CoinW Lists Solana-based Meme Coin RFC (Retard Finder Coin), Opens RFC/USDT Trading

RFC is a meme token built on the Solana blockchain, issued by the Twitter account @ifindretards. Known for its satirical commentary and engagement

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.

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.

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.

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.

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).

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

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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.

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.

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.

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.

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