Visible Digital Image Watermarking Using Single Candidate Optimizer

Abstract

With the advent of internet technologies, accessing information has become remarkably facile, while concurrently precipitating copyright conundrums. This predicament can be ameliorated by embedding copyright information within digital images, a methodology termed digital image watermarking. Artificial intelligence optimization algorithms are extensively employed in myriad problem-solving scenarios, yielding efficacious outcomes. This study proposes a visible digital image watermarking method utilizing the Single Candidate Optimizer (SCO). Contrary to many prevalent metaheuristic optimization algorithms, SCO, introduced in 2024, is not population-based. The fitness function of SCO is designed to maximize the resemblance between the watermarked image and both the host and watermark images. Experiments were conducted on images commonly utilized in image processing, and the results were evaluated using eight quality metrics. Additionally, the obtained numerical results were juxtaposed with those from well-known and widely-used genetic algorithms, differential evolution algorithms, and artificial bee colony optimization algorithms. The findings demonstrate that SCO outperforms the others in visible digital image watermarking. Furthermore, due to its non-population-based nature, SCO is significantly faster compared to its counterparts.

Keywords

• Single candidate optimizer
• Digital image watermarking
• Metaheuristic optimization algorithms

Tek Aday Optimizasyon Algoritması Kullanarak Görünür Dijital Resim Damgalama

Abstract

İnternet teknolojilerinin gelişmesiyle birlikte bilgiye erişim çok kolay hale gelirken diğer taraftan telif hakkı problemini ortaya çıkarmıştır. Bu problem dijital resimlerin içerisine telif hakkı ile ilgili bilgi gömerek çözülebilmektedir. Bu yöntemlere dijital resim damgalama denir. Yapay zeka optimizasyon algoritmaları bir çok problem çözümünde kullanılmakta ve etkili sonuçlar vermektedir. Bu çalışmada Single candidate optimizer (SCO) kullanarak görünür dijital görüntü damgalama yöntemi önerilir. 2024 yılında önerilen SCO, bir çok yaygın meta-sezgisel optimizasyon algoritmasının aksine popülasyon tabanlı değildir. SCO'nun amaç fonksiyonu olarak damgalanmış görüntünün hem barındırıcı hem de damga görüntüsü ile benzerliğini maksimize eden fonksiyon kullanılır. Deneyler görüntü işlemede yaygın kullanılan görüntülere uygulanmış ve sekiz adet kalite metriği kullanılarak sonuçlar değerlendirilmiştir. Ayrıca elde edilen sayısal sonuçlar iyi bilinen ve yaygın kullanılan genetik algoritma, diferansiyel gelişim algoritması ve yapay arı kolonisi optimizasyon algoritmaları ile karşılaştırılmıştır. Elde edilen bulgular SCO'nun görünür dijital görüntü damgalama için diğerlerinden daha iyi sonuç verdiğini göstermiştir. Ayrıca SCO'nun popülasyon tabanlı olmadığı için diğerlerine göre çok daha hızlı sonuç verdiği görülmüştür.

Keywords

• Tek aday optimizasyon algoritması
• dijital resim damgalama
• meta-sezgisel optimizasyon algoritmaları

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