GPT4-V is the new mode of GPT-4 that allows you to upload images as part of your conversations. It’s absolutely brilliant. It also provides a whole new set of vectors …
DALL·E 3
This page introduced DALL·E 3, OpenAI’s 2023 image generation system with stronger prompt adherence and ChatGPT integration. Explore ChatGPT Images 2.5.
Previewing GPT-5.6 Sol: a next-generation model
OpenAI previews GPT-5.6 Sol, a next-generation model with stronger capabilities in coding, science, and cybersecurity, paired with its most advanced safety stack.
Hello GPT-4o
We’re announcing GPT-4 Omni, our new flagship model which can reason across audio, vision, and text in real time.
ChatGPT Images 2.0 is a breakthrough that could fundamentally reshape graphic generation
OpenAI is adding reasoning and web search to its ChatGPT Images 2.0 image generator. The model can now create up to eight consistent images from a single prompt and handles text in general, and especially in non-Latin scripts, significantly better.
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
A Blog post by Rakshit Aralimatti on Hugging Face
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
CaMeL offers a promising new direction for mitigating prompt injection attacks
In the two and a half years that we’ve been talking about prompt injection attacks I’ve seen alarmingly little progress towards a robust solution. The new paper Defeating Prompt Injections …
Image Steganography
This is a client-side Javascript tool to steganographically hide images inside the lower "bits" of other images.
Universal AI Bypass: How Policy Puppetry Leaks System Prompts and Safety Data
HiddenLayer’s latest research uncovers a universal prompt injection bypass impacting GPT-4, Claude, Gemini, and more, exposing major LLM security gaps.
The Chilling Role of ChatGPT in Mass Shootings and Other Violence
Several attacks involving OpenAI’s chatbot—including Tumbler Ridge and FSU—raise urgent questions about the technology.
OpenAI unveils Lockdown Mode to protect sensitive data from prompt injection attacks | TechCrunch
Even with Lockdown Mode, ChatGPT could be vulnerable to prompt injections, but the goal is to reduce the likelihood that sensitive data gets shared in the process.
Introducing ChatGPT Images 2.0
ChatGPT Images 2.0 introduces a state-of-the-art image generation model with improved text rendering, multilingual support, and advanced visual reasoning.
Covolute on Twitter / X
spatial, multi-modal, multi-task IDE 🚀- track code by hovering application- chat with providing visual context- execute multiple tasks simultaneously- work on multiple pages/mods at same time pic.twitter.com/huuRDO8T0f— Covolute (@covolute) April 1, 2025
BytePlus Free Trial: 4K AI Image/Video Models + LLM Tokens | 200 Free Images
BytePlus free trial, Seedream 4.0, Seedance 1.0, DeepSeek V3.1, GPT-OSS-120B, free 4K AI images, AI video generation, BytePlus LLM tokens
proto IM | AIM-inspired video chat for the open web
Free video calls and buddy lists built on the AT Protocol. Like FaceTime, but it works on every device. Works with Blacksky, Bluesky, or any atproto account.