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Image steganography is applied to hide some secret information. Occasionally, steganography is used for malicious purposes to hide inappropriate information. In this paper, a new deep neural network was proposed to detect context-aware steganography techniques. In the proposed scheme, a high-boost filter was applied to alleviate the high-frequency while retaining the low-frequency details. The high-boost image was processed by thirty SRM high-pass filters to obtain thirty high-boost SRM filtered images. In the proposed CNN, two skip connections were used to collect information from multiple connections simultaneously. A clipped ReLU layer was considered in spite of the general ReLU layer. In constructing the CNN, a bottleneck approach was followed for an effective convolution. Only a single global average pooling layer was used to retain the complete flow of information. SVM was utilized instead of the softmax classifier to improve the detection accuracy. In the experimental results, the proposed technique was better than the existing techniques in terms of the detection accuracy and computational cost. The proposed scheme was verified on BOWS2 and BOSSBase datasets for the HILL, S-UNIWARD, and WOW context-aware steganography algorithms.