Current deep image steganography methods based on image-in-image concealment face challenges in practical applications of privacy protection and secure communication due to insufficient security of stego images and distortion in recovered secret images. To address these issues， a Conditional Generative Adversarial Network and Convolutional Block Attention Module-based image-in-image steganography method （CBAM-CGAN） was proposed. Firstly， a hybrid attention module was introduced into the generator network to enable the generator’s comprehensive learning of image features from both channel and spatial dimensions， thereby enhancing the visual quality of stego images. Secondly， residual connections were employed to reduce feature loss of secret images during network learning， and through adversarial training between the extractor and the discriminator， noise-free extraction of secret images was achieved. Finally， adversarial training between the generator and the steganalyzer was implemented to improve the stego image security. Experimental results on public datasets including COCO demonstrate that compared with steganography method StegGAN， the proposed steganography method achieves the Peak Signal-to-Noise Ratio （PSNR） improvements of 4.37 dB and 4.71 dB for stego and decrypted images， respectively， along with Structure Similarity Index Measure （SSIM） enhancements of 9.16% and 6.46%， respectively. For security， the proposed method has the detection Accuracy （Acc） against steganalyzer Ye-Net decreased by 9.35 percentage points with the False Negative Rate （FNR） increased by 12.01 percentage points. It can be seen that the proposed method ensures stego image security while achieving high-quality secret image recovery.