Herein, a method for unsharp masking (USM) sharpening detection is proposed. First, the local binary pattern (LBP) method is used to detect the edge features in an image. Then, a support vector machine is used for classification to detect whether the image is sharpened. Subsequently, the different LBP detection modes are compared in terms of the resulting sharpening intensity to select the optimal detection method. The experimental results show that the LBP method can achieve a relatively good USM sharpening detection effect. The rotation-invariant mode provides the best detection performance, providing a detection rate of up to 90% under the condition of weak sharpening, which is better than those achieved by the existing methods.