
\newpage

%% Please enter the Complete Abstract in the following in place of xxxxx.

\Abstract{

Image steganography is the practice of concealing secret information inside a digital image so that the existence of the message is difficult to detect. The central problem is to balance three competing goals—imperceptibility (minimal visible distortion), payload capacity (how many bits can be hidden), and robustness/security (reliable recovery under noise or processing and resistance to steganalysis) while meeting practical constraints such as a target bits-per-pixel (BPP) rate, with deterministic and key-controlled embedding.

This research introduces a novel steganography system that combines \texttt{HMAC-DRBG}-based keyed adaptive LSB embedding with \texttt{Attention map-in-header} technology generated through CNN and GAN models, along with genetic optimization for adaptive bit allocation to enhance the embedding process. The proposed system begins by generating pixel-level attention maps from cover image edges or textures using HED, RCF, VGG16, and ResNet50, before converting them into multiple threshold-based bit-allocation levels. The secret data size is then adjusted to achieve a specific bits-per-pixel (BPP) rate. The system also employs \texttt{HMAC-DRBG}-based keyed permutations to protect the secret image data from unauthorized access by applying stream-whitening techniques, while maintaining deterministic behavior for stego header and data placement. In addition, a nonce and the payload length are embedded in the stego header using threefold bit repetition with majority voting to enhance robust recovery capabilities. Furthermore, a mini-attention mechanism compresses a quantized low-resolution attention map to fit inside the header, enabling receivers to generate an approximate attention map when edge or texture detectors are available. A simple genetic algorithm is used to determine the optimal threshold and bit-allocation settings that maximize reconstruction quality, measured by PSNR and SSIM, under specified BPP constraints. Finally, at the sender side, the secret image is embedded into the cover image guided by the generated attention map to produce the stego image, while at the receiver side, the secret image is extracted from the stego image.

In addition to the proposed adaptive image steganography framework, this research integrates a Counting-Based Secret Sharing (CBSS) mechanism to further strengthen data confidentiality and reliability. CBSS divides a secret key into multiple shares and distributes them among authorized participants, allowing reconstruction of the original secret only when a predefined threshold number of shares are combined. This ensures secure group-oriented access and prevents any single participant from independently recovering the secret. The proposed CBSS adopts a block-masking strategy in which each block of the 64-bit secret key is XORed with a predefined sequence of ones, producing randomized and ambiguous shares that enhance confidentiality while minimizing computational cost. Experimental results demonstrate that increasing the block size reduces similarity among shares, thereby improving overall security.


{\color{blue}
This dissertation proposes a novel and integrated steganographic framework that combines content-aware attention maps, keyed adaptive LSB embedding, and genetic optimization. The core novelty lies in bridging coverless feature extraction (edges and textures) with embedding-based steganography through attention-guided bit allocation. In addition, the proposed framework introduces a robust header design and incorporates a counting-based secret sharing (CBSS) mechanism to enhance reliability and secure multi-party access. Collectively, these contributions advance the state of the art in secure, adaptive, and resilient image steganography.
}
}
\newpage
 \AbstractArabic{يعرف ال image steganography  بانه عملية اخفاء بيانات سرية داخل الصورة بحيث نقوم باخفاء وجود البيانات وليس تشفيرها.تتمثل المشكلة في هذا الحقل بكيفية ايجاد التوازن الصحيح بين ثلاثة عوامل هما الimperceptipility, capacity security .في هذا البحث قمنا ببناء وحساب ثم التنبؤ بخريطة تسمى ال ttention map باستخدام خوارزميات الذكاء الاصطناعي ثم قمنا بتخصيص عدد البتات لكل بكسل عبر عتبات محددة بحيث تُحسب سعة الإخفاء وتُعدّل الصورة السرية لتتناسب مع الهدف . تعتمد  عملية الاخفاء على وضع بتات في أقل البتات (LSB) مع ترتيب مواضع البتات بواسطة تتابع قابل لإعادة التوليد باستخدام HMAC-DRBG ومفتاح (keyed permutation) واختياريًا تبييض البتات (whitening) عبر تدفق HMAC —   بالاعتماد على خريطة انتباه مصغّرة داخل الهيدر" التي تضيف إلى الهيدر تمثيلاً مُكمَّمًا لصورة الانتباه المصغّرة (mini) بحيث يستطيع المستقبِل إعادة بناء خريطة انتباه تقريبية .الهيدر نفسه يحتوي على nonce وطول الرسالة ويُكرَّر كل بت ثلاث مرات لاستخراج أكثر موثوقية عبر تصويت الأغلبية. يتوفر أيضاً مُولّد إعدادات بسيط قائم على خوارزمية جينية (GA) للبحث عن عتبات وعدد بتات جيدين بناءً على مقاييس جودة مثل PSNR وSSIM.
 }
