State-of-the-art drone path planners enable drones to autonomously travel through obstacles in GPS-denied, uncharted, cluttered environments. However, our investigation shows that path planners fail to maneuver drones correctly in specific scenarios, leading to incidents such as collisions. To minimize such risks, drone path planners should be tested thoroughly against diverse scenarios before deployment. Existing research for testing drones to uncover safety-critical vulnerabilities is only focused on flight control programs and is limited in the capability to generate diverse obstacle scenarios for testing drone path planners. In this work, we propose DPFuzzer, an automated framework for testing drone path planners. DPFuzzer is an evolutionary algorithm (EA) based testing framework. It aims to uncover vulnerabilities in drone path planners by generating diverse critical scenarios that can trigger vulnerabilities. To better guide the critical scenario generation, we introduce Environmental Risk Factor (ERF), a metric we propose, to abstract potential safety threats of scenarios. We evaluate DPFuzzer on state-of-the-art drone path planners and the experimental result shows that DPFuzzer can effectively find diverse vulnerabilities. Additionally, we demonstrate that these vulnerabilities are exploitable in the real world.

Simulation is a vital method to test autonomous vehicle software. It provides a low cost and convenient virtual environment to verify the control program of autonomous vehicles. However, to deploy simulation, the vehicle software must either have built-in adaptability or undergo the source code level modification to adapt to simulators. Unfortunately, most autonomous vehicles on the market are not capable of adapting to simulators, and their source code is not available to the public. Therefore, we propose an innovative off-chip peripheral emulation-based methodology to assist drone software in adapting to simulators. The experiments demonstrate that our methodology can help simulate drones without modifying their software. This makes simulation more feasible for third-party analysts. Furthermore, we deployed a prototype called Dvatar and simulated a real-world drone using Dvatar for demonstration.

The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this article, termed as MU-TEIR. First, MU-TEIR employs a convolutional neural network VGG16 to extract image feature vectors and calculate the mean and variance of the feature vectors to construct the index, then encrypts index with the distributed two trapdoors public-key cryptosystem. After that, MU-TEIR protects image content by encrypting each image pixel with a standard stream cipher. Furthermore, MU-TEIR utilizes a watermark-based mechanism to prevent malicious query users from maliciously distributing images. Detailed security analysis shows that MU-TEIR protects the outsourced images and indexes security as well as query privacy, and can track malicious users. Experimental results verify effectiveness of MU-TEIR.

The increasing awareness in privacy has partly contributed to the renewed interest in privacy-preserving encrypted image retrieval, and designing for outsourced images stored on cloud servers, etc. However, there are some limitations in these existing schemes such as low retrieval accuracy, low retrieval efficiency, and less efficient result verification in the dynamic setting. Therefore, in this paper we present a novel Dynamic Verifiable Retrieval over Encrypted Images (DVREI) scheme. First, a pre-trained Convolutional Neural Network (CNN) model is utilized to extract image features to improve retrieval accuracy. Then, an encrypted index based on the K-means clustering algorithm is designed to improve retrieval efficiency. Finally, a dynamic verification tree based on the chameleon hash is used to verify the correctness of the retrieval results and support dynamic updates. We theoretically and experimentally evaluate the security and performance of DVREI to demonstrate its practicability.

With the emergence of intelligent terminals, the Content-Based Image Retrieval (CBIR) technique has attracted much attention from many areas (i.e., cloud computing, social networking services, etc.). Although existing privacy-preserving CBIR schemes can guarantee image privacy while supporting image retrieval, these schemes still have inherent defects (i.e., low search accuracy, low search efficiency, key leakage, etc.). To address these challenging issues, in this article we provide a similarity Search for Encrypted Images in secure cloud computing (called SEI). First, the feature descriptors extracted by the Convolutional Neural Network (CNN) model are used to improve search accuracy. Next, an encrypted hierarchical index tree by using$K$-means clustering based on Affinity Propagation (AP) clustering is devised, which can improve search efficiency. Then, a limited key-leakage k-Nearest Neighbor (kNN) algorithm is proposed to protect key from being completely leaked to untrusted image users. Finally, SEI is extended to further prevent image users’ search information from being exposed to the cloud server. Our formal security analysis proves that SEI can protect image privacy as well as key privacy. Our empirical experiments using a real-world dataset illustrate the higher search accuracy and efficiency of SEI.

With the advent of cloud computing, explosively increasing images are gradually outsourced to the cloud server for costs saving and feasibility. For security and privacy concerns, images (e.g., medical diagnosis, personal photos) should be encrypted before being outsourced. However, traditional encrypted image retrieval techniques still suffer from costly access control and low search accuracy. To solve these challenging issues, in this article, we first propose a Controllable encrypted cloud image Search scheme in Multi-user settings (namely CSM) by using the polynomial-based access strategy and proxy re-encryption technique. CSM achieves efficient access control and avoids heavy communication overhead caused by key transmission. Then, we improve the basic CSM to achieve malicious search user Tracing (namely TCSM) by utilizing the watermark technique, which can further prevent search users from illegally redistributing retrieved images to unauthorized search users. Our formal security analysis proves that our CSM (or TCSM) can guarantee the privacy of images, indexes, and search queries. Our empirical experiments using real-world datasets demonstrate the efficiency and high accuracy of our CSM (or TCSM) in practice.