I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen AI-enabled identity theft is getting too sophisticated to have predictable tells anymore, so experts recommend answering with a seemingly old-fashioned approach. Deepfakes and AI clones get more sophisticated and harder to detect. Advanced attacks involve long-term infiltration of a company's systems. Experts recommend low-tech security protocols that offer better defenses. In January 2024, an employee at professional services firm Arup joined a video call with someone they believed included the company's CFO.

Scammers are out of control. Every year, fraudsters and cybercriminals make billions by tricking people into parting with their cash. Romance fraud, business email compromise, investment scams, sextortion--the list of ways criminals prey on people is virtually endless and constantly changing. Add to that impersonation scams, where a criminal pretends to be someone known to their target and extracts money. There have been increasing calls for people, and particularly families, to create passphrases or passwords with each other.

This document outlines the Text-dependent Speaker Verification (TdSV) Challenge 2024, which centers on analyzing and exploring novel approaches for text-dependent speaker verification. The primary goal of this challenge is to motive participants to develop single yet competitive systems, conduct thorough analyses, and explore innovative concepts such as multi-task learning, self-supervised learning, few-shot learning, and others, for text-dependent speaker verification. Building upon the achievements of the Short-duration Speaker Verification (SdSV) Challenge 2020 and 2021, the TdSV Challenge 2024 focuses exclusively on text-dependent verification in two distinct scenarios. The first scenario involves conventional TdSV, while the second track entails speaker enrollment using user-defined passphrases. The evaluation dataset utilized for the challenge is derived from the second version of the versatile DeepMine dataset [1, 2]. For this challenge, Parts 1 and 3 of the dataset are employed. The subsequent section provides a comprehensive description of both tasks.

Web user data plays a central role in the ecosystem of pre-trained large language models (LLMs) and their fine-tuned variants. Billions of data are crawled from the web and fed to LLMs. How can \textit{\textbf{everyday web users}} confirm if LLMs misuse their data without permission? In this work, we suggest that users repeatedly insert personal passphrases into their documents, enabling LLMs to memorize them. These concealed passphrases in user documents, referred to as \textit{ghost sentences}, once they are identified in the generated content of LLMs, users can be sure that their data is used for training. To explore the effectiveness and usage of this copyrighting tool, we define the \textit{user training data identification} task with ghost sentences. Multiple datasets from various sources at different scales are created and tested with LLMs of different sizes. For evaluation, we introduce a last $k$ words verification manner along with two metrics: document and user identification accuracy. In the specific case of instruction tuning of a 3B LLaMA model, 11 out of 16 users with ghost sentences identify their data within the generation content. These 16 users contribute 383 examples to $\sim$1.8M training documents. For continuing pre-training of a 1.1B TinyLlama model, 61 out of 64 users with ghost sentences identify their data within the LLM output. These 64 users contribute 1156 examples to $\sim$10M training documents.

Voice recognition provides a number of important benefits over other kinds of identity authentication, including iris scans, face recognition, and fingerprint scans. To begin with, it is generally used for authentication on mobile phones, since all phones come equipped with microphones. Second, it is cost-effective to integrate into other devices like household appliances and autos [19]; third, it is convenient and familiar to the majority of consumers due to the fast expansion of IoT devices. Finally, it has been shown to be very accurate in some settings [20]. A client-side application and a server are commonly used in a voice authentication system.

Picking the perfect password comes down to a battle between two competing priorities: creating safe passwords that are lengthy and unique, and creating ones you can remember. You might think to yourself, I already have more passwords than I need! I've created passwords for years! But with the rise of password breaches, and with more passwords exposed that are linked to usernames, a solid password strategy is becoming more essential every day. We'll start out with the basics: the best ways to store passwords, and how to avoid using popular, easily-guessed passwords.

For the last 50 years, the fundamental and largely unchanged model for identifying and authenticating users has been based on the combination of a username and password, sometimes augmented with "second factor" techniques. While this approach has mostly served financial and other high-security industries well, it's increasingly shown to suffer from five drawbacks: For example, it's well known that "passphrases" are more secure than "passwords." But with advent of mobile apps, user preferences have shifted to make these the most frequently-used access modes, making passphrases more impractical. Even when services enforce their own complex password requirements, "forgotten username and password" reset mechanisms often fall back to less secure personal email accounts as the primary identity verification point. For example, an unattended laptop or misplaced mobile phone may provide a malicious user with a window of opportunity to access services the victim has already signed into. Fortunately, there is an emergent approach that can address these concerns by shifting emphasis from asking "do we recognize the user's username and password?"

Deep learning is still not a very common tool in speaker verification field. We study deep convolutional neural network performance in the text-prompted speaker verification task. The prompted passphrase is segmented into word states - i.e. digits -to test each digit utterance separately. We train a single high-level feature extractor for all states and use cosine similarity metric for scoring. The key feature of our network is the Max-Feature-Map activation function, which acts as an embedded feature selector. By using multitask learning scheme to train the high-level feature extractor we were able to surpass the classic baseline systems in terms of quality and achieved impressive results for such a novice approach, getting 2.85% EER on the RSR2015 evaluation set. Fusion of the proposed and the baseline systems improves this result.