You don't have to upload your video to the cloud or pay a monthly fee to secure your home. In the age of state surveillance, with big tech trampling our data privacy rights and gouging us for every penny, there are plenty of reasons to keep your security camera footage local. Whether you want to save money or ensure your video doesn't end up in the hands of persons (or AI) unknown, subscription-free security cameras are the way to go. The good news is that locally recording security cameras are better than ever. I've been testing security cameras for a decade, and the gap between the best cloud-connected and local cameras is closing. You don't necessarily have to give up the best features to shirk that subscription anymore.

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When you purchase through links in our articles, we may earn a small commission. BotsLab 4-Cam W510 System review: This security package doesn't deliver Four 4K cameras, a base station with expandable local storage, and no subscription required, So, what's the catch? This four-camera system impresses with solid video quality and expandable local storage, but only when those cameras are in such close range that they probably won't provide full coverage of your property. Outfitting your home with outdoor security cameras can get complicated--and expensive--quickly. Anyone looking for a shortcut on both fronts might consider one of BotsLab's W510 kits, bundles consisting of up to six 4K outdoor pan/tilt security cameras, solar panels to keep each camera's battery topped off, and a base station with 32GB of onboard storage (expandable up to 16TB with a user-supplied 2.5 hard drive).

Light up and secure your driveway, backyard, or porch with a floodlight security camera. Floodlight security cameras are a great way to light up your property. Shady areas around your home can make life easier for would-be burglars, and make it harder for you to plug in the car or take out the trash. Motion-triggered lighting is an essential minimum, but with a floodlight security camera, you get that a videofeed. Floodlight cameras are also far more configurable and reliable than lights; they let you check in on your property from the office or bed, and they can alert you to intruders. While this guide covers floodlight security cameras, we also have guides to the Best Outdoor Security Cameras, Best Indoor Security Cameras, and Best Video Doorbells .

When you purchase through links in our articles, we may earn a small commission. Most floodlight cams need hardwired power, limiting your installation options. This battery-powered model can go anywhere, and it has a solar panel, too! Single floodlight isn't as bright as you get with hardwired models Despite a couple of minor bugs, this low-cost, battery-powered floodlight camera knocks it out of the park in most respects. The "Kit" in TP-Link Tapo C615F Kit refers to the inclusion of a solar panel that comes with this full-featured security camera/floodlight combo to keep its battery charged.

Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning infinitesimal transformation operators (Lie group generators) via minimizing image reconstruction error. Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules. Here we propose a biologically plausible model of motion detection. We also adopt the transformation-operator approach but, instead of reconstruction-error minimization, start with a similarity-preserving objective function.

When you purchase through links in our articles, we may earn a small commission. This dual-4K-lens PoE turret camera gives you a full 180 degrees of coverage, night lighting, and AI detection in one tidy, affordable unit. The Annke FCD800 delivers sharp panoramic coverage, smart detection, and solid deterrence at a great price, making it an easy recommendation for anyone who needs to monitor a large area with a single, reliable camera on a tight budget and has the required infrastructure in place (or plans to add it). Not long ago, panoramic security camera coverage required installing multiple units and you'd still end up with blind spots. Then dual-lens models came along and promised to fix that by stitching two views into one wide shot.

As large AI models become increasingly valuable assets, the risk of model weight exfiltration from inference servers grows accordingly. An attacker controlling an inference server may exfiltrate model weights by hiding them within ordinary model outputs, a strategy known as steganography. This work investigates how to verify model responses to defend against such attacks and, more broadly, to detect anomalous or buggy behavior during inference. We formalize model exfiltration as a security game, propose a verification framework that can provably mitigate steganographic exfiltration, and specify the trust assumptions associated with our scheme. To enable verification, we characterize valid sources of non-determinism in large language model inference and introduce two practical estimators for them. We evaluate our detection framework on several open-weight models ranging from 3B to 30B parameters. On MOE-Qwen-30B, our detector reduces exfiltratable information to <0.5% with false-positive rate of 0.01%, corresponding to a >200x slowdown for adversaries. Overall, this work further establishes a foundation for defending against model weight exfiltration and demonstrates that strong protection can be achieved with minimal additional cost to inference providers.

Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the difficulty in defining and estimating heat-related mortality. Furthermore, establishing an early warning system imposes additional requirements, including data availability, spatial and temporal robustness, and decision costs. To address these challenges, we propose DeepTherm, a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history. By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves and other irregular events from all-cause mortality. We evaluated DeepTherm on real-world data across Spain. Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.

To enhance the performance of large language models (LLMs) in various domain-specific applications, sensitive data such as healthcare, law, and finance are being used to privately customize or fine-tune these models. Such privately adapted LLMs are regarded as either personal privacy assets or corporate intellectual property. Therefore, protecting model weights and maintaining strict confidentiality during deployment and distribution have become critically important. However, existing model formats and deployment frameworks provide little to no built-in support for confidentiality, access control, or secure integration with trusted hardware. Current methods for securing model deployment either rely on computationally expensive cryptographic techniques or tightly controlled private infrastructure. Although these approaches can be effective in specific scenarios, they are difficult and costly for widespread deployment. In this paper, we introduce CryptoTensors, a secure and format-compatible file structure for confidential LLM distribution. Built as an extension to the widely adopted Safetensors format, CryptoTensors incorporates tensor-level encryption and embedded access control policies, while preserving critical features such as lazy loading and partial deserialization. It enables transparent decryption and automated key management, supporting flexible licensing and secure model execution with minimal overhead. We implement a proof-of-concept library, benchmark its performance across serialization and runtime scenarios, and validate its compatibility with existing inference frameworks, including Hugging Face Transformers and vLLM. Our results highlight CryptoTensors as a light-weight, efficient, and developer-friendly solution for safeguarding LLM weights in real-world and widespread deployments.