X to block Grok AI's undressing feature | Digital Dilemma The mother of one of Elon Musk's children is suing his artificial intelligence company, saying its Grok chatbot allowed users to generate sexually-exploitative deepfake images of her that have caused her humiliation and emotional distress. The lawsuit was filed just before California Attorney General Rob Bonta sent a cease-and-desist letter to Musk's xAI company demanding that it stop the creation and distribution of Grok-generated nonconsensual sexualised imagery . Ashley St Clair, a writer and political commentator, alleges in a lawsuit filed on Thursday in New York City against xAI that she was the victim of sexualised deepfake images generated by Grok. St Clair, who is the mother of Musk's 16-month-old son, Romulus, said she reported the images to Musk's X social media platform, which hosts Grok, after they began appearing last year and asked that they be removed. The platform replied that the images did not violate its policies, she said.

Indonesia has become the first country in the world to block Elon Musk's Grok chatbot over the risk of fake, AI-generated pornographic content. The country's communication and digital affairs minister said on Saturday that "the practice of non-consensual sexual deepfakes" is a "serious violation of human rights, dignity, and the security of citizens in the digital space". The move comes a day after Grok limited image generation and editing features on Musk's social media platform X to paying subscribers as it sought to tamp down mounting criticism over the deepfakes. Musk has been threatened with fines as several countries are pushing back publicly against Grok, which allowed users to alter online images to remove the subjects' clothes. The billionaire has said anyone using Grok to create illegal content would face the same consequences as uploading such material directly. But European officials and tech campaigners slammed this week's move to limit the AI tool's features to paying subscribers on X, saying it failed to address their concerns.

Abstract--Detecting deepfake images is crucial in combating misinformation. We present a lightweight, generalizable binary classification model based on EfficientNet-B6, fine-tuned with transformation techniques to address severe class imbalances. By leveraging robust preprocessing, oversampling, and optimization strategies, our model achieves high accuracy, stability, and generalization. While incorporating Fourier transform-based phase and amplitude features showed minimal impact, our proposed framework helps non-experts to effectively identify deepfake images, making significant strides toward accessible and reliable deepfake detection. Advances in synthetic data generation have introduced both opportunities and challenges, particularly with the rise of deep-fake technology.

Tokyo police believe the man made about 20,000 sexually explicit images of 262 women, such as actors and idols, and amassed sales of ¥1.2 million between October last year and September this year. Tokyo police have arrested a 31-year-old man for allegedly creating fake sexual images of female celebrities with generative artificial intelligence technology and displaying them online, it was learned Thursday. It is the first time that police in Japan have cracked down on sexual deepfake images of celebrities created with generative AI. The suspect, Hiroya Yokoi of the city of Akita, has admitted he began making deepfakes to earn a small amount of money, which he used to cover living expenses and repay a student loan. Authorities believe Yokoi made a total of about 20,000 sexually explicit images of 262 women, such as actors, television personalities and idols, and amassed sales of ¥1.2 million between October last year and September this year.

The rise of Deepfake technology to generate hyper-realistic manipulated images and videos poses a significant challenge to the public and relevant authorities. This study presents a robust Deepfake detection based on a modified Vision Transformer(ViT) model, trained to distinguish between real and Deepfake images. The model has been trained on a subset of the OpenForensics Dataset with multiple augmentation techniques to increase robustness for diverse image manipulations. The class imbalance issues are handled by oversampling and a train-validation split of the dataset in a stratified manner. Performance is evaluated using the accuracy metric on the training and testing datasets, followed by a prediction score on a random image of people, irrespective of their realness. The model demonstrates state-of-the-art results on the test dataset to meticulously detect Deepfake images.

The online safety regulator wants a 450,000 maximum penalty imposed on a man who posted deepfake images of prominent Australian women to a website, in the first case of its kind heard in an Australian court. The eSafety commissioner has launched proceedings against Anthony Rotondo over his failure to remove "intimate images" of several prominent Australian women from a deepfake pornography website. The federal court has kept the names of the women confidential. Rotondo initially refused to comply with the order while he was based in the Philippines, the court heard, but the commissioner launched the case once he returned to Australia. Rotondo posted the images to the MrDeepFakes website, which has since been shut down.

In this paper, we introduce the idea of using adversarially-generated samples of the input images that were classified as deepfakes by a detector, to form perturbation masks for inferring the importance of different input features and produce visual explanations. We generate these samples based on Natural Evolution Strategies, aiming to flip the original deepfake detector's decision and classify these samples as real. We apply this idea to four perturbation-based explanation methods (LIME, SHAP, SOBOL and RISE) and evaluate the performance of the resulting modified methods using a SOTA deepfake detection model, a benchmarking dataset (FaceForensics++) and a corresponding explanation evaluation framework. Our quantitative assessments document the mostly positive contribution of the proposed perturbation approach in the performance of explanation methods. Our qualitative analysis shows the capacity of the modified explanation methods to demarcate the manipulated image regions more accurately, and thus to provide more useful explanations.

For the pope, it was the wrong kind of madonna. The pop legend, she of the 80's anthem Like a Prayer, has stirred controversy in recent weeks by posting deepfake images on social media which show the pontiff embracing her. It has fanned the flames of a debate which is already raging over the creation of AI art in which Pope Francis plays a symbolic, and unwilling, role. The head of the Catholic church is used to being the subject of AI-generated fakery. One of the defining images of the AI boom was Francis in a Balenciaga puffer jacket.

Australians could see fewer deepfake images of celebrities being hauled off in handcuffs, or promoting a fraudulent cryptocurrency investment on Facebook, after Meta launched a new one-stop shop for banks to share information on scams that has blocked 8,000 pages and 9,000 celebrity scams in its first six months of operation. From January to August 2024, Australians reported 43.4m in losses from scams on social media to Scamwatch, with close to 30m relating to fake investment scams. Meta, the parent company of Facebook and Instagram, has faced pressure from politicians and regulators in the past few years to tackle the plague of scams featuring deepfake images of public figures such as David Koch, Gina Rinehart, Anthony Albanese, Larry Emdur, Guy Sebastian and others which are used to promote investment scams. The company is being sued by the mining magnate Andrew Forrest over the company's alleged failure to tackle scams using his image. Meta announced on Wednesday it had partnered with the Australian Financial Crimes Exchange (AFCX) to launch the Fraud Intelligence Reciprocal Exchange (Fire) that provides a dedicated reporting channel for scams between Meta and financial providers of the victims of the scams.

In August, X, the social media company once known as Twitter, publicly released Grok 2, the latest iteration of its AI chatbot. With limited guardrails, Grok has been responsible for pushing misinformation about elections and allowing users to make life-like artificial intelligence-generated images – otherwise known as deepfakes – of elected officials in ethically questionable positions. The social media giant has started to rectify some of its problems. After election officials in Michigan, Minnesota, New Mexico, Pennsylvania and Washington wrote to X head Elon Musk alleging that the chatbot produced false information about state ballot deadlines, X now points users to Vote.gov for election-related questions. But when it comes to deepfakes, that's a different story.