{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:08:12Z","timestamp":1770289692739,"version":"3.49.0"},"reference-count":40,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":34,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study introduces a novel attention\u2010guided Discrete Wavelet Transform (DWT)\u2010based steganography framework, named Attention\u2010Guided Feature Perturbation (AGFP), which integrates deep visual attention maps with transform\u2010domain embedding to enhance imperceptibility, robustness, and steganalysis resistance. Unlike recent deep\u2010learning\u2010based steganographic systems such as iSCMIS, JARS\u2010Net, and RMSteg, which achieve high visual fidelity but are susceptible to statistical detection, AGFP perturbs only those wavelet coefficients that are identified as perceptually and statistically stable by attention mechanisms extracted from pre\u2010trained CNN models (VGG19, ResNet50, AlexNet, and GoogLeNet). The proposed method is evaluated on the USC\u2010SIPI dataset and the BOSSBase 1.01 benchmark. Experimental results show that AGFP achieves PSNR values between 64.29 and 55.43\u00a0dB and SSIM scores between 0.9999 and 0.9989 across varying payloads, indicating consistently high visual quality. While iSCMIS reports slightly higher PSNR and SSIM values, AGFP significantly outperforms all compared methods in bit error rate (BER)\u2014achieving 0.01\u20130.12, compared to 0.45\u20130.47 for iSCMIS, 0.31\u20130.37 for RMSteg, and 0.57\u20130.75 for JARS\u2010Net. Furthermore, AGFP attains the lowest RS, SPA, and SRM steganalysis detection scores among both classical and deep\u2010learning\u2010based systems. These results confirm that AGFP offers a more balanced and secure steganographic solution, combining high imperceptibility with substantially enhanced robustness and detectability resistance, positioning it as a strong alternative to recent deep\u2010learning\u2010based steganographic frameworks.<\/jats:p>","DOI":"10.1049\/ipr2.70288","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:42:36Z","timestamp":1770237756000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AGFP: A Deep Attention\u2010Guided Framework for DWT\u2010Based Image Steganography"],"prefix":"10.1049","volume":"20","author":[{"given":"Taner","family":"Cevik","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Istanbul Rumeli University  Istanbul Turkiye"}]},{"given":"Nazife","family":"Cevik","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Istanbul Arel University  Istanbul Turkiye"}]},{"given":"Ali","family":"Pasaoglu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Istanbul Rumeli University  Istanbul Turkiye"}]},{"given":"Fatih","family":"Sahin","sequence":"additional","affiliation":[{"name":"Department of Software Engineering Istanbul Topkapi University  Istanbul Turkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0354-9344","authenticated-orcid":false,"given":"Farzad","family":"Kiani","sequence":"additional","affiliation":[{"name":"Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University  Istanbul Turkiye"}]},{"given":"Muhammet Sait","family":"Ag","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Istanbul Rumeli University  Istanbul Turkiye"}]}],"member":"265","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.1998.4655281"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/93.959097"},{"key":"e_1_2_12_4_1","unstructured":"N.Provos \u201cDefending Against Statistical Steganalysis \u201d inProceedings of the 10th USENIX Security Symposium (Usenix Association 2001) 323\u2013333."},{"key":"e_1_2_12_5_1","volume-title":"Digital Watermarking","author":"Cox I. 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