Digital fingerprinting is an emerging digital forensic technology that has been developed to detect digital forgeries and identify the pirates who leak the copies. Understanding the weaknesses and limitations of existing fingerprinting schemes and designing anti-forensic approaches play an important role in the development of digital fingerprinting. In this paper, we propose a support vector machine (SVM)-based anti-forensic method capable of removing the fingerprints from the previously marked images for spread-spectrum fingerprinting. We first estimate the parameters of the embedded fingerprint superposed on the frequency coefficients of the original signal. Then, we select the best basis through wavelet packet decomposition for thresholding the fingerprinted coefficients. Furthermore, an SVM-based classifier is used to measure the existence of the pirates' fingerprints. The experimental results show that the proposed method is more effective than the other examined approaches. About three pieces of fingerprinted content are able to interrupt the fingerprinting system that accommodates thousands of users. Meanwhile, high fidelity of the attacked content is retained. Copyright © 2012 John Wiley & Sons, Ltd.