A JPEG image steganalysis scheme based on joint discrete cosine transform (DCT) domain features is proposed. Intrinsic characteristics of DCT coefficients, such as histogram, intrablock correlation, and interblock correlation, are exploited to construct three feature sets. Support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. First, the three feature sets are investigated separately to reveal their individual capability of attacking steganographic methods. Second, the feature sets are combined to form a joint feature set with better performance. Experimental results demonstrate that all three feature sets individually succeed in attacking the four typical steganographic tools to some extent, with the intrablock feature set performing the best. Furthermore, the comparison experiments show that the jointed feature set not only outperforms the three individual feature sets but also proves to be better than a previous state-of-the-art steganalysis method.