Although the perceptual image hashing is one of the promising techniques for image authentication, most existing methods cannot well distinguish content changing manipulations from acceptable content preserving modifications, especially when the size of the manipulated area is relatively small. In this regard, a new image hash algorithm is proposed to enhance the tamper detection capability by employing one of the most well-known local feature descriptors, Histogram of Oriented Gradients (HOG), for the feature extraction method. In this paper, image intensity transform using a random number generator, HOG feature computation, Successive Mean Quantization Transform (SMQT), and bit-level permutation are utilized to obtain a secure and robust hash value. Additionally, the performance of the proposed method is measured, and compared with existing algorithms by the Receiver Operating Characteristics (ROC) analysis.