Trump rushed into Iran crisis meeting as insider warns'strike within hours' 'Deeply concerned' King Charles backs Andrew investigation after royal's arrest and says the'law must take its course' Model agency boss who'scouted' victims for Epstein was secretly planning to testify against him... only to suddenly change his mind before meeting chillingly similar fate to notorious pedophile The monarchy has survived wars and countless crises... but this is why it may not survive Andrew's arrest - and why the rift at the heart of the family is about to get so much worse: ROBERT JOBSON Widower whose wife set herself on fire after alleged affair with married congressman finally breaks silence to reveal their texts... and heartbreaking video of her death Whereabouts of Andrew's ex-wife and daughters remain unknown as former prince is arrested over public misconduct claims Andrew'pushed through' appointment of Jeffery Epstein's fixer to board of Windsor Castle trust despite opposition Virginia Giuffre's family hail Andrew's arrest and say'he was never a prince' But countless women (and some husbands) are secretly getting it for thrilling sex side effects... risking a truly putrid complication FBI'has names and photos of people who may be masked suspect caught on surveillance video outside Nancy Guthrie's home' How DID Beatrice afford her 20s jet-set lifestyle? US assembles the most aerial firepower since Iraq War as Trump prepares to strike Iran'in just DAYS'... and president is'choosing between two devastating options of attack' The side-effects were unbearable and I swore off the drug forever. This is the simple diet that helped me shed the pounds... and I'm not alone. The Prince and Princess of Wales express support for King Charles' statement after Andrew's arrest Jason Bateman says he quit cocaine and alcohol to ease'tension' in his marriage Humiliating real reason Mia Goth left Shia LaBeouf: What'friends and lovers' are all saying behind his back... after Mardi Gras brawl What happens now Andrew Mountbatten-Windsor has been arrested? I went into early menopause in my 30s.

Trump backflips on visas for foreign workers as he stuns Laura Ingraham with brutal take on America's workforce Trump bristles as Laura Ingraham awkwardly asks if '24 karat gold' adorning Oval Office is from Home Depot Bill de Blasio's secret lover REVEALED: Latina politician'thinks she's won the jackpot'... but friends spill wild details on scandalous tryst Cloud hangs over Don Jr's socialite girlfriend Bettina Anderson... as insiders reveal the MAGA titan she was originally chasing Trump'obesity ban' preventing overweight foreigners from entering the US revealed in bombshell leak Michelle Obama's greatest fear is being seen as her true self. Well, let's cut the bottomless victimhood and race-baiting... here it is: MAUREEN CALLAHAN Sydney Sweeney's movie flop sparks cruel remark about her body from plus-size model James Van Der Beek forced to auction off Dawson's Creek keepsakes amid'expensive' colon cancer treatments JFK's grandson Jack Schlossberg, 32, announces bid for congress as he responds to critics who call him'crazy' Trump pardons trail runner facing charges for'illegal' shortcut during run Colorado woman's body is found feet from her home seven years after she disappeared CHERYL HINES: The day I met Donald Trump in a private hotel suite... and broke out in hives Diddy seen in first prison mugshot... and once-groomed rapper has now gone completely gray Can YOU spot the fake faces? Can YOU spot the fake faces? Can you tell the difference between a real face and one generated by artificial intelligence ( AI)? According to a new study, the answer is probably'no'.

Multi-face deepfake videos are becoming increasingly prevalent, often appearing in natural social settings that challenge existing detection methods. Most current approaches excel at single-face detection but struggle in multi-face scenarios, due to a lack of awareness of crucial contextual cues. In this work, we develop a novel approach that leverages human cognition to analyze and defend against multi-face deepfake videos. Through a series of human studies, we systematically examine how people detect deepfake faces in social settings. Our quantitative analysis reveals four key cues humans rely on: scene-motion coherence, inter-face appearance compatibility, interpersonal gaze alignment, and face-body consistency. Guided by these insights, we introduce \textsf{HICOM}, a novel framework designed to detect every fake face in multi-face scenarios. Extensive experiments on benchmark datasets show that \textsf{HICOM} improves average accuracy by 3.3\% in in-dataset detection and 2.8\% under real-world perturbations. Moreover, it outperforms existing methods by 5.8\% on unseen datasets, demonstrating the generalization of human-inspired cues. \textsf{HICOM} further enhances interpretability by incorporating an LLM to provide human-readable explanations, making detection results more transparent and convincing. Our work sheds light on involving human factors to enhance defense against deepfakes.

In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing limitations in current detectors related to variations across different types of fake face generation techniques, where ``cross-domain" refers to the diverse range of these deepfakes, each considered a separate domain. BENet's core feature is a bias expansion module based on autoencoders. This module maintains genuine facial features while enhancing differences in fake reconstructions, creating a reliable bias for detecting fake faces across various deepfake domains. We also introduce a Latent-Space Attention (LSA) module to capture inconsistencies related to fake faces at different scales, ensuring robust defense against advanced deepfake techniques. The enriched LSA feature maps are multiplied with the expanded bias to create a versatile feature space optimized for subtle forgeries detection. To improve its ability to detect fake faces from unknown sources, BENet integrates a cross-domain detector module that enhances recognition accuracy by verifying the facial domain during inference. We train our network end-to-end with a novel bias expansion loss, adopted for the first time, in face forgery detection. Extensive experiments covering both intra and cross-dataset demonstrate BENet's superiority over current state-of-the-art solutions.

Most previous deepfake detection methods bent their efforts to discriminate artifacts by end-to-end training. However, the learned networks often fail to mine the general face forgery information efficiently due to ignoring the data hardness. In this work, we propose to introduce the sample hardness into the training of deepfake detectors via the curriculum learning paradigm. Specifically, we present a novel simple yet effective strategy, named Dynamic Facial Forensic Curriculum (DFFC), which makes the model gradually focus on hard samples during the training. Firstly, we propose Dynamic Forensic Hardness (DFH) which integrates the facial quality score and instantaneous instance loss to dynamically measure sample hardness during the training. Furthermore, we present a pacing function to control the data subsets from easy to hard throughout the training process based on DFH. Comprehensive experiments show that DFFC can improve both within- and cross-dataset performance of various kinds of end-to-end deepfake detectors through a plug-and-play approach. It indicates that DFFC can help deepfake detectors learn general forgery discriminative features by effectively exploiting the information from hard samples.

Artificial intelligence (AI) is now so sophisticated that we can't tell the difference between fake faces and snaps of real people, a new study warns. In experiments with US citizens, more people thought AI-generated faces were human than the faces of real people. Experts are concerned that'hyper-realistic' imagery could be fueling misinformation and identity theft online by creating authentic-looking profiles of people. In the study, the researchers compared five AI faces with five human faces. So, can you tell which of these people are real?

The exploitation of Deepfake techniques for malicious intentions has driven significant research interest in Deepfake detection. Deepfake manipulations frequently introduce random tampered traces, leading to unpredictable outcomes in different facial regions. However, existing detection methods heavily rely on specific forgery indicators, and as the forgery mode improves, these traces become increasingly randomized, resulting in a decline in the detection performance of methods reliant on specific forgery traces. To address the limitation, we propose Recap, a novel Deepfake detection model that exposes unspecific facial part inconsistencies by recovering faces and enlarges the differences between real and fake by mapping recovered faces. In the recovering stage, the model focuses on randomly masking regions of interest (ROIs) and reconstructing real faces without unpredictable tampered traces, resulting in a relatively good recovery effect for real faces while a poor recovery effect for fake faces. In the mapping stage, the output of the recovery phase serves as supervision to guide the facial mapping process. This mapping process strategically emphasizes the mapping of fake faces with poor recovery, leading to a further deterioration in their representation, while enhancing and refining the mapping of real faces with good representation. As a result, this approach significantly amplifies the discrepancies between real and fake videos. Our extensive experiments on standard benchmarks demonstrate that Recap is effective in multiple scenarios.

The ability of generative models to produce highly realistic synthetic face images has raised security and ethical concerns. As a first line of defense against such fake faces, deep learning based forensic classifiers have been developed. While these forensic models can detect whether a face image is synthetic or real with high accuracy, they are also vulnerable to adversarial attacks. Although such attacks can be highly successful in evading detection by forensic classifiers, they introduce visible noise patterns that are detectable through careful human scrutiny. Additionally, these attacks assume access to the target model(s) which may not always be true. Attempts have been made to directly perturb the latent space of GANs to produce adversarial fake faces that can circumvent forensic classifiers. In this work, we go one step further and show that it is possible to successfully generate adversarial fake faces with a specified set of attributes (e.g., hair color, eye size, race, gender, etc.). To achieve this goal, we leverage the state-of-the-art generative model StyleGAN with disentangled representations, which enables a range of modifications without leaving the manifold of natural images. We propose a framework to search for adversarial latent codes within the feature space of StyleGAN, where the search can be guided either by a text prompt or a reference image. We also propose a meta-learning based optimization strategy to achieve transferable performance on unknown target models. Extensive experiments demonstrate that the proposed approach can produce semantically manipulated adversarial fake faces, which are true to the specified attribute set and can successfully fool forensic face classifiers, while remaining undetectable by humans. Code: https://github.com/koushiksrivats/face_attribute_attack.

Two Stanford researchers have fallen down a LinkedIn rabbit hole, finding over 1,000 fake profiles using AI-generated faces at the bottom. Renée DiResta and Josh Goldstein from the Stanford Internet Observatory made the discovery after DiResta was messaged by a profile reported to belong to a "Keenan Ramsey". It looked like a normal software sales pitch at first glance, but upon further investigation, it became apparent that Ramsey was an entirely fictitious person. While the picture appeared to be a standard corporate headshot, it also included multiple red flags that point to it being an AI-generated face like those generated by websites like This Person Does Not Exist. DiResta was specifically tipped off by the alignment of Ramsey's eyes (the dead center of the photo), her earrings (she was only wearing one) and her hair, several bits of which blurred into the background.

Fake faces created by artificial intelligence (AI) look more trustworthy than faces of real people, a worrying new study reveals. Researchers conducted several experiments to see whether fake faces created by machine learning frameworks were able to fool humans. They found synthetically generated faces are not only highly photo realistic, but are also nearly indistinguishable from real faces - and are even judged to be more trustworthy. Due to the results, the researchers are calling for safeguards to prevent'deepfakes' from circulating online. Deepfakes have already been used for so-called'revenge porn', fraud and propaganda, leading to misplaced identity and the spread of fake news.