Chen Wang, et al., reports that The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the fine-grained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous
(i.e., defined as any metal and liquid object) without penetrating into the user’s privacy through physically opening the baggage.
Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object’s dimension (i.e., liquid volume and metal object’s shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16 ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.
This paper explores the feasibility of using off-the-shelf WiFi signals to detect suspicious objects (i.e., metal and liquid objects) hidden in baggage without penetrating into the user’s privacy. Our solution is timely as it demonstrates the possibility to reuse the prevalent WiFi technology to perform suspicious objects detection at every public area vulnerable to adversarial activities without requiring the installation of high-cost security-checking infrastructures. The designed system can also estimate the risk level of the target object through object imaging to estimate the shape/volume of the metal/liquid objects. Specifically, we deploy two different system setups for separating the refraction signals and the reflection signals from the CSI complex and recognizing the object’s material and shape, respectively. Our system removes the raw phase noise in CSI and reconstructs the CSI complex, which can robustly capture the dominant interference caused by the suspicious material even when the object is hidden in the baggage.
We also derive the reflection channel from CSI complex that can enable us to estimate the object’s shape and volume at a fine level using the long-wavelength WiFi signals.
Extensive experiments are conducted with 15 objects and 6 bags over a 6-month period. The results show that our system can detect over 95% dangerous objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16 ml and 0.5 cm when estimating the shape/volume of the metal/liquid object, respectively.
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