This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

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.

Blind navigation is an accessibility application that enables blind to use an android Smartphone in an easy way for indoor navigation with instructions in audio form. We have proposed a prototype which is an indoor navigation application for blinds that uses QR codes. It is developed for android Smart phones and does not require any additional hardware for navigation. It provides automatic navigational assistance on pre-defined paths for blind. QR codes are placed on the floor sections after specific distance that acts as an input for current location detection and navigation. Whenever a QR code is scanned it provides the user with the information of the current location and asks the user to select the destination and then offers optimal and shortest path using path finding algorithms. During navigation whenever the deviation from the proposed path is detected it prompts the user and guides back to the right path by comparing the current path with the generated path. All of the instructions throughout the application are provided in audio form to the user. The interface of the application is well built for blinds which makes the smart phones user-friendly and useable for blind people. The user interacts with the application through a specific set of user-friendly gestures for specific inputs and operations. At the end, we have performed comparison between different state of art approaches and concluded that our approach is more user friendly, cost effective and produced more accurate results.

The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust protection than metadata-based approaches--which can be easily stripped--current watermarking techniques remain vulnerable to forgery, creating risks of misattribution that can damage the reputations of AI model developers and the rights of digital artists. These vulnerabilities arise from two key issues: (1) content-agnostic watermarks, which, once learned or leaked, can be transferred across images to fake attribution, and (2) reliance on detector-based verification, which is unreliable since detectors can be tricked. We present MetaSeal, a novel framework for content-dependent watermarking with cryptographic security guarantees to safeguard image attribution. Our design provides (1) forgery resistance, preventing unauthorized replication and enforcing cryptographic verification; (2) robust, self-contained protection, embedding attribution directly into images while maintaining resilience against benign transformations; and (3) evidence of tampering, making malicious alterations visually detectable. Experiments demonstrate that MetaSeal effectively mitigates forgery attempts and applies to both natural and AI-generated images, establishing a new standard for secure image attribution.