A lightweight robust image hash based on random tensors and angle features for IoT devices

Image hashing can be extensively used in image forensics, and the lightweight image hash suitable for IoT smart devices plays an important role of integrity verification for the image obtained by these devices; however, the robust image hashing used in smart devices with resource-constrained is rarely discussed. Therefore, a lightweight perceptual robust image hashing scheme based on random tensor and angle features is introduced in this paper. Specifically, the global features are obtained through quantization of DCT coefficients generated from expanding of two skillfully designed three-order tensors; in order to obtain image local features, the energies of some non-overlapping image blocks are first computed, and then, local features are achieved through calculating the angle features. At last, the global and local features are transformed into the corresponding hash. Large quantities of experiments on four datasets are implemented to justify the effectiveness of the proposed method. Some comparisons on length of hashing, TPR, FPR, ROC curve, AUC value and collision probability show that the suggested proposal achieves moderate length, higher detection accuracy and better balance between robustness and discrimination than the state-of-the-art algorithms, and the tests in smart phone-based data collecting system show that the proposed technique is feasible, and it has potential application value for image verification in IoT devices.