This paper provides a comprehensive overview of the first international challenge on steganalysis, known as BOSS (Break Our Steganographic System). The challenge was organized to investigate the robustness of content-adaptive steganography and to encourage the development of new steganalysis techniques. The paper details the motivations behind the contest, its rules, and the steganographic algorithm used, HUGO (Highly Undetectable steGO). HUGO is a spatial-domain content-adaptive algorithm designed to minimize embedding impact and was found to be largely resistant to steganalysis up to 0.4 bits per pixel in 512x512 grayscale images. The paper also describes the image databases created for the contest, including the BOSSBase and BOSSRank databases, which were used for training and testing steganalyzers, respectively. A significant challenge for participants was the cover-source mismatch, where images from different sources were used for training and testing, leading to reduced detection accuracy. The paper presents detailed analysis of the results submitted to the challenge, highlighting the performance of different steganalyzers and their vulnerabilities to the cover-source mismatch. Key findings include the development of two steganalyzers with similar performance, suggesting the potential for combining their strengths. The paper also discusses the need for future research to address issues such as cover-source mismatch and high false positive rates, and the importance of fair methodologies for comparing steganalyzers. Overall, the BOSS challenge has contributed significantly to advancing the field of steganalysis and steganography.This paper provides a comprehensive overview of the first international challenge on steganalysis, known as BOSS (Break Our Steganographic System). The challenge was organized to investigate the robustness of content-adaptive steganography and to encourage the development of new steganalysis techniques. The paper details the motivations behind the contest, its rules, and the steganographic algorithm used, HUGO (Highly Undetectable steGO). HUGO is a spatial-domain content-adaptive algorithm designed to minimize embedding impact and was found to be largely resistant to steganalysis up to 0.4 bits per pixel in 512x512 grayscale images. The paper also describes the image databases created for the contest, including the BOSSBase and BOSSRank databases, which were used for training and testing steganalyzers, respectively. A significant challenge for participants was the cover-source mismatch, where images from different sources were used for training and testing, leading to reduced detection accuracy. The paper presents detailed analysis of the results submitted to the challenge, highlighting the performance of different steganalyzers and their vulnerabilities to the cover-source mismatch. Key findings include the development of two steganalyzers with similar performance, suggesting the potential for combining their strengths. The paper also discusses the need for future research to address issues such as cover-source mismatch and high false positive rates, and the importance of fair methodologies for comparing steganalyzers. Overall, the BOSS challenge has contributed significantly to advancing the field of steganalysis and steganography.