Animated QR codes present an exciting frontier for dynamic content delivery and digital interaction. However, despite their potential, there has been no prior work focusing on the generation of animated QR codes that are both visually appealing and universally scannable. In this paper, we introduce AnimateQR, the first generative framework for creating animated QR codes that balance aesthetic flexibility with scannability. Unlike previous methods that focus on static QR codes, AnimateQR leverages hierarchical luminance guidance and progressive spatiotemporal control to produce high-quality dynamic QR codes. Our first innovation is a multi-scale hierarchical control signal that adjusts luminance across different spatial scales, ensuring that the QR code remains decodable while allowing for artistic expression. The second innovation is a progressive control mechanism that dynamically adjusts spatiotemporal guidance throughout the diffusion denoising steps, enabling fine-grained balance between visual quality and scannability. Extensive experimental results demonstrate that AnimateQR achieves state-of-the-art performance in both decoding success rates (96% vs. 56% baseline) and visual quality (user preference: 7.2 vs. 2.3 on a 10-point scale). Codes are availble at https://github.com/mulns/AnimateQR.

Animated QR codes present an exciting frontier for dynamic content delivery and digital interaction. However, despite their potential, there has been no prior work focusing on the generation of animated QR codes that are both visually appealing and universally scannable.

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A Conspiracy Theory About QR Codes Has Led to Chaos Ahead of Georgia's Midterms The state of Georgia banned the use of QR codes for elections, based in part on the assertions of a man who's boosted false claims about Israel and 9/11. Now no one knows how ballots will be counted. QR codes are at the center of the latest conspiracy theory in Georgia's elections. And it's largely thanks to Garland Favorito, a man who has spent decades trying to get people to listen to his conspiracy theories about insecure voting machines being used to rig elections in Georgia. When Georgia became the epicenter of election denial conspiracy theories in 2020, Favorito became an overnight superstar in the election denial community, and an integral part of the vast network of groups across the country that sprang up to promote the baseless claim that US elections are rigged.

A text scam impersonating state courts demands drivers pay $6.99 for fake traffic violations via QR codes. The scheme has hit residents in at least eight U.S. states.

Globally, individuals and organizations employ Quick Response (QR) codes for swift and convenient communication. Leveraging this, cybercriminals embed falsify and misleading information in QR codes to launch various phishing attacks which termed as Quishing. Many former studies have introduced defensive approaches to preclude Quishing such as by classifying the embedded content of QR codes and then label the QR codes accordingly, whereas other studies classify them using visual features (i.e., deep features, histogram density analysis features). However, these approaches mainly rely on black-box techniques which do not clearly provide interpretability and transparency to fully comprehend and reproduce the intrinsic decision process; therefore, having certain obvious limitations includes the approaches' trust, accountability, issues in bias detection, and many more. We proposed QRïS, the pioneer method to classify QR codes through the comprehensive structural analysis of a QR code which helps to identify phishing QR codes beforehand. Our classification method is clearly transparent which makes it reproducible, scalable, and easy to comprehend. First, we generated QR codes dataset (i.e. 400,000 samples) using recently published URLs datasets [1], [2]. Then, unlike black-box models, we developed a simple algorithm to extract 24 structural features from layout patterns present in QR codes. Later, we train the machine learning models on the harvested features and obtained accuracy of up to 83.18%. To further evaluate the effectiveness of our approach, we perform the comparative analysis of proposed method with relevant contemporary studies. Lastly, for real-world deployment and validation, we developed a mobile app which assures the feasibility of the proposed solution in real-world scenarios which eventually strengthen the applicability of the study.

Former GOP operative Scott Leiendecker just bought Dominion Voting Systems, giving him ownership of voting systems used in 27 states. The news last week that Dominion Voting Systems was purchased by the founder and CEO of Knowink, a Missouri-based maker of electronic poll books, has left election integrity activists confused over what, if anything, this could mean for voters and the integrity of US elections. The company, acquired by Scott Leiendecker, a former Republican Party operative and election director in Missouri before founding Knowink, said in a press release that he was rebranding Dominion, which has headquarters in Canada and the United States, under the name Liberty Vote "in a bold and historic move to transform and improve election integrity in America" and to distance the company from false allegations made previously by President Donald Trump and his supporters that the company had rigged the 2020 presidential election to give the win to President Joe Biden. The Liberty release said that the rebranded company will be 100 percent American owned, that it will have a "paper ballot focus" that leverages hand-marked paper ballots, will "prioritize facilitating third-party auditing," and is "committed to domestic staffing and software development." The press release provided no details, however, to explain what this means in practice.

Our pipeline introduces three innovative components.

Despite tremendous success in many application scenarios, the training and inference costs of using deep learning are also rapidly increasing over time. The lottery ticket hypothesis (L TH) emerges as a promising framework to leverage a special sparse subnetwork (i.e., winning ticket) instead of a full model for both training and inference, that can lower both costs without sacrificing the performance.