IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks
Infrared (IR) images record the temperature radiation distribution of the object being captured. The hue and color difference in the image reflect the caloric and temperature difference, respectively. However, due to the thermal diffusion effect, the target information in IR images can be relatively...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/5/171 |
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| author | Yu Bai Li Li Shanqing Zhang Jianfeng Lu Ting Luo |
| author_facet | Yu Bai Li Li Shanqing Zhang Jianfeng Lu Ting Luo |
| author_sort | Yu Bai |
| collection | DOAJ |
| description | Infrared (IR) images record the temperature radiation distribution of the object being captured. The hue and color difference in the image reflect the caloric and temperature difference, respectively. However, due to the thermal diffusion effect, the target information in IR images can be relatively large and the objects’ boundaries are blurred. Therefore, IR images may undergo some image enhancement operations prior to use in relevant application scenarios. Furthermore, Infrared Enhancement (IRE) algorithms have a negative impact on the watermarking information embedded into the IR image in most cases. In this paper, we propose a novel multi-scale robust watermarking model under IRE attack, called IEWNet. This model trains a preprocessing module for extracting image features based on the conventional Undecimated Dual Tree Complex Wavelet Transform (UDTCWT). Furthermore, we consider developing a noise layer with a focus on four deep learning and eight classical attacks, and all of these attacks are based on IRE algorithms. Moreover, we add a noise layer or an enhancement module between the encoder and decoder according to the application scenarios. The results of the imperceptibility experiments on six public datasets prove that the Peak Signal to Noise Ratio (PSNR) is usually higher than 40 dB. The robustness of the algorithms is also better than the existing state-of-the-art image watermarking algorithms used in the performance evaluation comparison. |
| format | Article |
| id | doaj-art-c66dc348c60c41d6b6eeb8fae1fb311e |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-c66dc348c60c41d6b6eeb8fae1fb311e2025-08-20T03:47:54ZengMDPI AGJournal of Imaging2313-433X2025-05-0111517110.3390/jimaging11050171IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement AttacksYu Bai0Li Li1Shanqing Zhang2Jianfeng Lu3Ting Luo4School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Science and Technology, Ningbo University, Ningbo 315211, ChinaInfrared (IR) images record the temperature radiation distribution of the object being captured. The hue and color difference in the image reflect the caloric and temperature difference, respectively. However, due to the thermal diffusion effect, the target information in IR images can be relatively large and the objects’ boundaries are blurred. Therefore, IR images may undergo some image enhancement operations prior to use in relevant application scenarios. Furthermore, Infrared Enhancement (IRE) algorithms have a negative impact on the watermarking information embedded into the IR image in most cases. In this paper, we propose a novel multi-scale robust watermarking model under IRE attack, called IEWNet. This model trains a preprocessing module for extracting image features based on the conventional Undecimated Dual Tree Complex Wavelet Transform (UDTCWT). Furthermore, we consider developing a noise layer with a focus on four deep learning and eight classical attacks, and all of these attacks are based on IRE algorithms. Moreover, we add a noise layer or an enhancement module between the encoder and decoder according to the application scenarios. The results of the imperceptibility experiments on six public datasets prove that the Peak Signal to Noise Ratio (PSNR) is usually higher than 40 dB. The robustness of the algorithms is also better than the existing state-of-the-art image watermarking algorithms used in the performance evaluation comparison.https://www.mdpi.com/2313-433X/11/5/171infrared imagesimage enhancementmulti-scalerobust watermarkingnoise layerenhancement sub-network |
| spellingShingle | Yu Bai Li Li Shanqing Zhang Jianfeng Lu Ting Luo IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks Journal of Imaging infrared images image enhancement multi-scale robust watermarking noise layer enhancement sub-network |
| title | IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks |
| title_full | IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks |
| title_fullStr | IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks |
| title_full_unstemmed | IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks |
| title_short | IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks |
| title_sort | iewnet multi scale robust watermarking network against infrared image enhancement attacks |
| topic | infrared images image enhancement multi-scale robust watermarking noise layer enhancement sub-network |
| url | https://www.mdpi.com/2313-433X/11/5/171 |
| work_keys_str_mv | AT yubai iewnetmultiscalerobustwatermarkingnetworkagainstinfraredimageenhancementattacks AT lili iewnetmultiscalerobustwatermarkingnetworkagainstinfraredimageenhancementattacks AT shanqingzhang iewnetmultiscalerobustwatermarkingnetworkagainstinfraredimageenhancementattacks AT jianfenglu iewnetmultiscalerobustwatermarkingnetworkagainstinfraredimageenhancementattacks AT tingluo iewnetmultiscalerobustwatermarkingnetworkagainstinfraredimageenhancementattacks |