The misuse of deep learning-based facial manipulation poses a potential threat to civil rights. To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process by adding invisible adversarial perturbations into images, making the forged output unconvincing to the observer. However, their non-directional disruption of the output may result in the retention of identity information of the person in the image, leading to stigmatization of the individual. In this paper, we propose a novel universal framework for combating facial manipulation, called ID-Guard. Specifically, this framework requires only a single forward pass of an encoder-decoder network to generate a cross-model universal adversarial perturbation corresponding to a specific facial image. To ensure anonymity in manipulated facial images, a novel Identity Destruction Module (IDM) is introduced to destroy the identifiable information in forged faces targetedly. Additionally, we optimize the perturbations produced by considering the disruption towards different facial manipulations as a multi-task learning problem and design a dynamic weights strategy to improve cross-model performance. The proposed framework reports impressive results in defending against multiple widely used facial manipulations, effectively distorting the identifiable regions in the manipulated facial images. In addition, our experiments reveal the ID-Guard's ability to enable disrupted images to avoid face inpaintings and open-source image recognition systems.

In tabular biomedical data analysis, tuning models to high accuracy is considered a prerequisite for discussing feature importance, as medical practitioners expect the validity of feature importance to correlate with performance. In this work, we challenge the prevailing belief, showing that low-performing models may also be used for feature importance. We propose experiments to observe changes in feature rank as performance degrades sequentially. Using three synthetic datasets and six real biomedical datasets, we compare the rank of features from full datasets to those with reduced sample sizes (data cutting) or fewer features (feature cutting). In synthetic datasets, feature cutting does not change feature rank, while data cutting shows higher discrepancies with lower performance. In real datasets, feature cutting shows similar or smaller changes than data cutting, though some datasets exhibit the opposite. When feature interactions are controlled by removing correlations, feature cutting consistently shows better stability. By analyzing the distribution of feature importance values and theoretically examining the probability that the model cannot distinguish feature importance between features, we reveal that models can still distinguish feature importance despite performance degradation through feature cutting, but not through data cutting. We conclude that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. This paper demonstrates the potential for utilizing feature importance analysis alongside statistical analysis to compare features relatively, even when classifier performance is not satisfactory.

Witnesses described unthinkable scenes of heavy flooding in northern Italy as Storm Boris continued its journey across Europe on Thursday. People were seen climbing on roofs to escape the water as buildings collapsed in Traversara di Bagnacavallo. The Italian emergency services carried out helicopter rescues after what one eyewitness said was 36 hours of rain. Storm Boris had earlier swept across Poland, the Czech Republic, Romania and Austria, killing at least 23 people. One of Kyiv's main government buildings was hit in overnight missile and drone strikes by Russia.

Surgeon'became robotic' to treat sheer volume of wounded Lebanese A Lebanese surgeon has described how the sheer volume of severe wounds from two days of exploding device attacks forced him to act robotic just to be able to keep working. Surgeon Elias Jaradeh said he treated women and children but most of the patients he saw were young men. The surgeon said a large proportion were "severely injured" and many had lost the sight in both eyes. The dead and injured in Lebanon include fighters from Hezbollah - the Iranian backed armed group which has been trading cross-border fire with Israel for months and is classed as a terrorist organisation by the UK and the US. But members of their families have also been killed or wounded, along with innocent bystanders.

Debbie Weinstein, Google's UK managing director, says'proactive action' is needed to keep the country at the forefront of new tech. Debbie Weinstein, Google's UK managing director, says'proactive action' is needed to keep the country at the forefront of new tech. Google says UK risks being'left behind' in AI race without more data centres Thu 19 Sep 2024 14.08 EDTFirst published on Thu 19 Sep 2024 11.00 EDT The company pointed to research showing that the UK is ranked seventh on a global AI readiness index for data and infrastructure, and called for a number of policy changes. Google's UK managing director, Debbie Weinstein, said that the government "sees the opportunity" in AI but needs to introduce more policies boosting its deployment. "We have a lot of advantages and a lot of history of leadership in this space, but if we do not take proactive action, there is a risk that we will be left behind," she said.

Get ready for the return of the annual energy summit in Berlin on October 10. Returning for its second edition this October in Berlin, the WIRED & Octopus Energy Tech Summit is bringing together Europe's leading experts and visionaries in the green energy sector to explore how to accelerate the creation of a fully carbon-free energy system. Last year's summit focused on the urgent need for green technology in the wake of the energy crisis. Audiences heard from business leaders, startup founders, politicians, inventors, and even an astronaut. This year, energy leaders from across the EU will meet to carve the path to a rapid global energy transition.

'You can harness the power of death in a controlled way.' Breakthroughs, discoveries, and DIY tips sent every weekday. While only about 10 percent of the roughly 4,000 known snake species have venom that can harm a human, using genetics to determine which snakes could be deadly could speed up developing better treatments for bites. A new tool called VenomCap can help scientists hone in on venom at a genetic level, so we can know which ones are likely carrying deadly toxins. The method is detailed in a study published September 19 in the journal Molecular . "We've developed a tool that can tell us which venom-producing genes are present across an entire snake family in one fell swoop," Sara Ruane, a study co-author and the Assistant Curator of Herpetology at the Field Museum in Chicago, said in a statement .

The Pixel 9 is Google's cheaper top-end phone, which keeps its standout design. The Pixel 9 is Google's cheaper top-end phone, which keeps its standout design. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Google's cheapest Pixel 9 offers almost everything that makes its top-flight sibling one of the best smaller phones available, cutting a few key ingredients to price match Apple and Samsung.

Improving the automatic and timely recognition of construction and demolition waste (C&DW) composition is crucial for enhancing business returns, economic outcomes, and sustainability. Technologies like computer vision, artificial intelligence (AI), robotics, and internet of things (IoT) are increasingly integrated into waste processing to achieve these goals. While deep learning (DL) models show promise in recognising homogeneous C&DW piles, few studies assess their performance with mixed, highly contaminated material in commercial settings. Drawing on extensive experience at a C&DW materials recovery facility (MRF) in Sydney, Australia, we explore the challenges and opportunities in developing an advanced automated mixed C&DW management system. We begin with an overview of the evolution of waste management in the construction industry, highlighting its environmental, economic, and societal impacts. We review various C&DW analysis techniques, concluding that DL-based visual methods are the optimal solution. Additionally, we examine the progression of sensor and camera technologies for C&DW analysis as well as the evolution of DL algorithms focused on object detection and material segmentation. We also discuss C&DW datasets, their curation, and innovative methods for their creation. Finally, we share insights on C&DW visual analysis, addressing technical and commercial challenges, research trends, and future directions for mixed C&DW analysis. This paper aims to improve the efficiency of C&DW management by providing valuable insights for ongoing and future research and development efforts in this critical sector.

In the rapidly evolving field of financial sentiment analysis, the efficiency and accuracy of predictive models are critical due to their significant impact on financial markets. Transformer based models like BERT and large language models (LLMs) like GPT-4, have advanced NLP tasks considerably. Despite their advantages, BERT-based models face challenges with computational intensity in edge computing environments, and the substantial size and compute requirements of LLMs limit their practical deployment. This study proposes leveraging the generative capabilities of LLMs, such as GPT-4 Omni, to create synthetic, domain-specific training data. This approach addresses the challenge of data scarcity and enhances the performance of smaller models by making them competitive with their larger counterparts. The research specifically aims to enhance FinBERT, a BERT model fine-tuned for financial sentiment analysis, and develop TinyFinBERT, a compact transformer model, through a structured, two-tiered knowledge distillation strategy. Using data augmented by GPT-4 Omni, which involves generating new training examples and transforming existing data, we significantly improved the accuracy of FinBERT, preparing it to serve as a teacher model. This enhanced FinBERT then distilled knowledge to TinyFinBERT, employing both GPT-4 Omni and GPT-3.5 Turbo augmented data. The distillation strategy incorporated both logit and intermediate layer distillation. The training and evaluation of TinyFinBERT utilized the PhraseBank dataset and the FiQA 2018 Task1 dataset, achieving performance comparable to FinBERT while being substantially smaller and more efficient. This research demonstrates how LLMs can effectively contribute to the advancement of financial sentiment analysis by enhancing the capabilities of smaller, more efficient models through innovative data augmentation and distillation techniques.