Measuring trustworthiness of image data in the internet of things environment

Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.Doctor of Philosoph