<![CDATA[Digital images are widely utilized as carriers for covert communications through steganography. However, this technique is increasingly exploited to conceal malicious payloads, such as malware, without altering visual appearances. While the Least Significant Bit (LSB) method remains popular due to its high visual imperceptibility, existing steganalysis techniques often fail to recover hidden content when insertion parameters are unknown. This study evaluates the effectiveness of the Force Decode strategy as an exploratory blind steganalysis approach to extract LSB payloads from PNG images without prior knowledge of embedding configurations. Employing a quantitative comparative experimental design, the proposed system was implemented in Python. The method extracts LSBs across the red, green, and blue (RGB) color channels, reconstructs binary sequences through eight pixel-scanning orientations, and converts them into ASCII text. Evaluation was conducted on fifteen stego-image datasets representing five insertion complexity levels, ranging from single-channel alphabetic messages to multi-channel random combinations of letters, numbers, and symbols. The experimental results demonstrate that Force Decode successfully generated textual output across all datasets regardless of insertion complexity. Low-complexity datasets yielded highly legible messages with minimal noise, whereas high-complexity scenarios produced a hybrid of meaningful text fragments and random characters. Despite increased noise in complex configurations, the method consistently revealed latent payload content without prior parameter knowledge. These findings confirm that Force Decode is a viable and effective exploratory steganalysis technique for PNG images, laying a practical foundation for future research on reliable blind LSB extraction and automated interpretation systems.]]>
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