An IoT device is a piece of hardware, typically a sensor, that transmits data from one place to another over the internet. Types of IoT devices include simple (often wireless) sensors, actuators, as well as more sophisticated computerized devices.
A digital twin (DT) is the software representation of a physical object. At a bare minimum, a DT must include the unique identifier of the physical object it represents. However, it only starts fulfilling its purpose once additional information — such as sensory information (position, temperature, humidity, etc.) and/or its actuation capabilities (turn lamp on/off, etc.) — is added. The DT will often include additional auxiliary data, such as the device’s firmware version, configuration, calibration, and setpoint data.
When it comes to actuation, we often talk about the DT as a “shadow” of its physical representation in order to highlight the fact that actuations are always transactional. For instance, the DT’s intent to change its device state (turn it off or on) requires a particular command to be sent to the device, which after successful completion of the actuation needs to be communicated back to the caller (the DT).
A digital twin is sometimes referred to as the representation of an IoT device, which is not exactly the same. Let us take a closer look at the two categories, as the difference between them is actually bigger than one might think. Read More
Daily Archives: July 28, 2021
Lucasfilm hires YouTuber who used deepfake to improve ‘The Mandalorian’
Luke Skywalker’s CGI face in the character’s The Mandalorian cameo was met with a lot of criticism, and fans even tried to fix the scene with various tools and programs. One of those fans did so well, Lucasfilm has hired him to help it ensure its upcoming projects won’t feature underwhelming de-aging and facial visual effects. That fan is a YouTuber known as Shamook, who uses deepfake technology to improve upon bad CG effects and to put actors in shows and movies they never starred in. Read More
An AI Road Map Starts With Data
Several years into what many people expected to be an AI revolution, there is a nagging sense that we are at a crossroads. Artificial intelligence is an evolutionary step forward for business optimization strategies — and rightly so — but the companies that saw AI as the path to the promised land could be forgiven for thinking that the hype has outweighed successful implementation.
Granted, there are numerous organizations that have integrated AI into their business processes, and it is already a routine part of software development, cybersecurity, natural language processing and robotic process automation (RPA).
And yes, making AI a priority in terms of scalability and an accelerated time to market has shown a modicum of success. Two years ago, for example, AI adoption rates reportedly grew by 270% from 2015 to 2018, according to Gartner, and some observers enthusiastically predicted that a brave new world was already here.
But adoption doesn’t equal success. Read More
EvilModel: Hiding Malware Inside of Neural Network Models
Delivering malware covertly and detection-evadingly is critical to advanced malware campaigns. In this paper, we present a method that delivers malware covertly and detection evadingly through neural network models. Neural network models are poorly explainable and have a good generalization ability. By embedding malware into the neurons, malware can be delivered covertly with minor or even no impact on the performance of neural networks. Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method. With the widespread application of artificial intelligence, utilizing neural networks becomes a forwarding trend of malware. We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks. Read More
#adversarial, #cyber
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