Multi-step or hybrid deepfakes, created by sequentially applying different deepfake creation methods such as Face-Swapping, GAN-based generation, and Diffusion methods, can pose an emerging and unforseen technical challenge for detection models trained on single-step forgeries. While prior studies have mainly focused on detecting isolated single manipulation, little is known about the detection model behavior under such compositional, hybrid, and complex manipulation pipelines. In this work, we introduce \textbf{FakeChain}, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. Using this approach, we analyze detection performance and spectral properties across hybrid manipulation at different step, along with varying generator combinations and quality settings. Surprisingly, our findings reveal that detection performance highly depends on the final manipulation type, with F1-score dropping by up to \textbf{58.83\%} when it differs from training distribution. This clearly demonstrates that detectors rely on last-stage artifacts rather than cumulative manipulation traces, limiting generalization. Such findings highlight the need for detection models to explicitly consider manipulation history and sequences. Our results highlight the importance of benchmarks such as FakeChain, reflecting growing synthesis complexity and diversity in real-world scenarios. Our sample code is available here\footnote{https://github.com/minjihh/FakeChain}.

In the wake of a fabricated explosion image at the Pentagon, an ability to discern real images from fake counterparts has never been more critical. Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. Our method, UGAD, encompasses three key detection steps: First, we transform the RGB images into YCbCr channels and apply an Integral Radial Operation to emphasize salient radial features. Secondly, the Spatial Fourier Extraction operation is used for a spatial shift, utilizing a pre-trained deep learning network for optimal feature extraction. Finally, the deep neural network classification stage processes the data through dense layers using softmax for classification. Our approach significantly enhances the accuracy of differentiating between real and AI-generated images, as evidenced by a 12.64% increase in accuracy and 28.43% increase in AUC compared to existing state-of-the-art methods.

Scientists keep taking their own lives, and no one knows why. That's the central mystery at the start of 3 Body Problem, the new Netflix series based on a trilogy of sci-fi novels by Chinese author Cixin Liu. But it soon unfolds into something far grander: There's a mysterious VR video game, flashbacks to revolutionary China, shady billionaires, and strange cults. Liu's novels are beloved in China and have a smaller but similarly dedicated following among English-language readers, but they are hard science fiction--heavy on concept, light on character. More than once in the series, someone resorts to wheeling out a chalkboard to make their point, and there are scenes in the books that seem impossible to film: multidimensional structures collapsing in on themselves, a computer made up of millions of soldiers, nano-wires cutting through steel, diamond, flesh.

Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors. Figure 1: Illustration of the face detected from different engines.

The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices. The paper discusses how deepfakes can be used to impersonate in gaming scenarios, how online meetings in the metaverse open the door for impersonation, and how virtual offices in the metaverse lack physical authentication, making it easier for attackers to impersonate someone. The implications of these security concerns are discussed in relation to the confidentiality, integrity, and availability (CIA) triad. The paper further explores related issues such as the darkverse, and digital cloning, as well as regulatory and privacy concerns associated with addressing security threats in the virtual world.

Jensen Huang getting carried away about an emerging technology is nothing new. This time last year, the charismatic and excitable co-founder and CEO of chip design giant Nvidia was telling anyone who'd listen about the potential of the metaverse (or the Omniverse, as Nvidia's marketing department prefers to call it). Since then, the metaverse bubble has suffered a slow puncture, and Huang is back to evangelising about one of his favourite topics: artificial intelligence. Describing the growth in power of generative AI systems like GPT-4 – the model that powers OpenAI's tools such as ChatGPT – as a "new era of computing", Huang told investors on his company's most recent earnings call that AI was at an "inflection point", stating that businesses have "an urgency to develop and deploy new AI strategies". However, Huang added that he believes many companies face "an insurmountable obstacle" in getting access to the resources and skills needed to make AI work, which is why, he says, Nvidia is getting into the services business.

This work proposes the Bregman-Tweedie classification model and analyzes the domain structure of the extended exponential function, an extension of the classic generalized exponential function with additional scaling parameter, and related high-level mathematical structures, such as the Bregman-Tweedie loss function and the Bregman-Tweedie divergence. The base function of this divergence is the convex function of Legendre type induced from the extended exponential function. The Bregman-Tweedie loss function of the proposed classification model is the regular Legendre transformation of the Bregman-Tweedie divergence. This loss function is a polynomial parameterized function between unhinge loss and the logistic loss function. Actually, we have two sub-models of the Bregman-Tweedie classification model; H-Bregman with hinge-like loss function and L-Bregman with logisticlike loss function. Although the proposed classification model is nonconvex and unbounded, empirically, we have observed that the H-Bregman and L-Bregman outperform, in terms of the Friedman ranking, logistic regression and SVM and show reasonable performance in terms of the classification accuracy in the category of the binary linear classification problem. Keywords: Extended exponential function, convex function of Legendre type, Bregman-Tweedie divergence, Bregman-Tweedie classification model, hinge loss, logistic loss.

Yet like a handful of local dating apps in this traditionally conservative country, Tinder's biggest challenge appears to be getting enough women to sign on. In many parts of India, arranged marriages--as opposed to what Indians call "love marriages"--remain the norm, and dating in any form still carries a stigma, particularly for women. Indian women who have dabbled with dating apps complain they get overwhelmed by all the attention that comes with the surplus of men. "The barrage of messages that hits your inbox is like a swarm of locusts," said Anushree Majumdar, age 33, in Mumbai. She uses dating apps only occasionally, she said, and can understand why women are intimidated by them.

Artificial intelligence helps create your own personal assistant in the job search. Have you ever wished you had a personal assistant to help you find a new job or move up in your career? Well, now you can have one, thanks to the chatbot. Chatbots know how to interact with people, because they either follow a human-composed set of rules or learn how to communicate via artificial intelligence (AI) technologies. Bots that follow rules are more limited and can only answer things they've been programmed to understand.

An adaptive online algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. Its efficiency is demonstrated for a non-stationary blind separation task of acoustic signals.