--Phishing is the most prevalent type of cyber-attack today and is recognized as the leading source of data breaches with significant consequences for both individuals and corporations. Web-based phishing attacks are the most frequent with vectors such as social media posts and emails containing links to phishing URLs that once clicked on render host systems vulnerable to more sinister attacks. Research efforts to detect phishing URLs have involved the use of supervised learning techniques that use large amounts of data to train models and have high computational requirements. They also involve analysis of features derived from vectors including email contents thus affecting user privacy. Additionally, they suffer from a lack of resilience against evolution of threats especially with the advent of generative AI techniques to bypass these systems as with AI-generated phishing URLs. Unsupervised methods such as clustering techniques have also been used in phishing detection in the past, however, they are at times unscalable due to the use of pair-wise comparisons. They also lack high detection rates while detecting phishing campaigns. In this paper, we propose an unsupervised learning approach that is not only fast but scalable, as it does not involve pair-wise comparisons. It is able to detect entire campaigns at a time with a high detection rate while preserving user privacy; this includes the recent surge of campaigns with targeted phishing URLs generated by malicious entities using generative AI techniques.

Content credentials are based on C2PA, an internet protocol that uses cryptography to securely label images, video, and audio with information clarifying where they came from--the 21st-century equivalent of an artist's signature. Although Adobe had already integrated the credentials into several of its products, including Photoshop and its own generative AI model Firefly, Adobe Content Authenticity allows creators to apply them to content regardless of whether it was created using Adobe tools. The company is launching a public beta in early 2025. The new app is a step in the right direction toward making C2PA more ubiquitous and could make it easier for creators to start adding content credentials to their work, says Claire Leibowicz, head of AI and media integrity at the nonprofit Partnership on AI. "I think Adobe is at least chipping away at starting a cultural conversation, allowing creators to have some ability to communicate more and feel more empowered," she says. "But whether or not people actually respond to the'Do not train' warning is a different question."

WASHINGTON, Feb 1 (Reuters) - U.S. investors including the investment arms of Intel Corp (INTC.O) and Qualcomm Inc (QCOM.O) accounted for nearly a fifth of investments in Chinese artificial intelligence companies from 2015 to 2021, a report showed on Wednesday. The document, released by CSET, a tech policy group at Georgetown University, comes amid growing scrutiny of U.S. investments in AI, Quantum and semiconductors, as the Biden administration prepares to unveil new restrictions on U.S. funding of Chinese tech companies. According to the report, 167 U.S. investors took part in 401 transactions, or roughly 17% of the investments into Chinese AI companies in the period. Those transactions represented a total $40.2 billion in investment, or 37% of the total raised by Chinese AI companies in the 6-year period. It was not clear from the report, which pulled information from data provider Crunchbase, what percentage of the funding came from the U.S. firms.

Researchers from France have devised a method for identifying newly-registered domains that are likely to be used in a'hit and run' fashion by high-volume email spammers – sometimes, even before the spammers have sent out one unwanted email. The technique is based on analysis of the way that that the Sender Policy Framework (SPF), a method of verifying email provenance, has been set up on newly-registered domains. Thanks to the use of passive DNS (Domain Name System) sensors, the researchers were able to obtain near real-time DNS data from Seattle-based company Farsight, yielding SPF activity for TXT records for a range of domains. Using a class weight algorithm originally designed for processing imbalanced medical data, and implemented in the scikit-learn machine learning Python library, the researchers were able to detect three quarters of the pending spam domains within moments, or even in advance of their operation. 'With a single request to the TXT record, we detect 75% of the spam domains, possibly before the start of the spam campaign.

The United States Defense Department (DoD) has added more than a dozen Chinese companies, including the world's largest drone manufacturer, to a blacklist of firms with alleged ties to the Chinese military, clearing the way for restrictions on their business. Shenzhen-based DJI Technology, which is estimated to control more than half of the global market for commercial drones, is among the 13 firms added to the blacklist released by the Pentagon on Wednesday. The blacklist grants the US president authority to impose sanctions against companies deemed to have connections to the Chinese military. The announcement comes after the US Treasury Department last year banned US-based persons from trading shares of DJI and seven other Chinese companies over their alleged involvement in the surveillance of ethnic minority Uighurs in China's far-western region of Xinjiang. BGI Genomics Co, a genetic testing company; CRRC Corp, which manufactures rolling stock; and Zhejiang Dahua Technology, a Hangzhou-based surveillance equipment maker, were also added to the updated list.

Like tiny gears inside a watch, algorithms execute well-defined tasks within more complicated programs. They're ubiquitous, and in part because of this, they've been painstakingly optimized over time. When a programmer needs to sort a list, for example, they'll reach for a standard "sort" algorithm that's been used for decades. Now researchers are taking a fresh look at traditional algorithms, using the branch of artificial intelligence known as machine learning. Their approach, called algorithms with predictions, takes advantage of the insights machine learning tools can provide into the data that traditional algorithms handle.

Just weeks after the U.S. placed a unit of SenseTime Group Inc. on a blacklist for alleged human rights violations, the firm is about to make founder Tang Xiao'ou one of the world's richest people. China's largest artificial intelligence company priced its initial public offering at HK$3.85 (49 cents) per share, raising $5.55 billion. That was the bottom of the expected range, but a signal that despite increased tensions with the U.S. and Beijing's crackdown on tech giants, the country, including its vast surveillance machinery, continues to churn out huge fortunes and massive gains for venture capitalists. Tang, 53, a Massachusetts Institute of Technology graduate and information engineering professor at the Chinese University of Hong Kong, holds a 21% stake in the company and is worth $3.4 billion, according to the Bloomberg Billionaires Index. A representative for SenseTime declined to comment on Tang's net worth.

In many ML interview rounds, candidates are asked to demonstrate their coding skills w.r.t. These kind of rounds helps to identify both coding skills as well as ML skills required to be a top notch ML engineer in some of the top companies. Let's look at some of the common ML coding problems asked in such interviews: Given the API rand7() that generates a uniform random integer in the range [1, 7], write a function rand10() that generates a uniform random integer in the range [1, 10]. You can only call the API rand7(), and you shouldn't call any other API. Please do not use a language's built-in random API.

On Monday Chinese Artificial intelligence and facial recognition firm SenseTime was forced to postpone its Initial Public Offering (IPO) in Hong Kong after it was on Friday added to a U.S government blacklist which bans Americans from investing in the firm or buying securities from it. The blacklisting would have rocked confidence in the IPO and lost it money, forcing it to tactically retreat until the dust settles. As the firm does most of its business in China, the loss of the United States is not expected to cause significant disruption, and it may seek to double down by seeking a second listing in the STAR market some point. However, it is important to understand why the United States is targeting such a firm, of which was first added to the commerce department entity list in 2019 alongside a host of other companies. That being, as a Chinese Company in the field of Artificial intelligence, it is operating in a domain which the US has grew to consider as a critical and strategic field of technology which it sees as intrinsic to its own geopolitical competition it is waging against China.

Chinese artificial intelligence start-up SenseTime Group has postponed its $767m Hong Kong initial public offering (IPO) after being placed on a US investment blacklist. SenseTime said it remained committed to completing the offering and would publish a supplemental prospectus and an updated listing timetable. Reuters first reported earlier on Monday the company's plan to withdraw the offering and update its prospectus to include the potential impact of the US investment ban, with the aim of relaunching the IPO process. SenseTime had planned to sell 1.5 billion shares in a price range of HK$3.85 ($0.49) to HK$3.99 ($0.51), according to its regulatory filings. That would raise up to $767m, a figure that had already been trimmed earlier this year from a $2bn target.