Image steganography, the covert embedding of data into digital pictures, represents a threat to the safeguarding of sensitive information and the gathering of intelligence. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation. Conversely, the hider who demands privacy must carefully examine a means to guarantee stealth. A rigorous framework for analysis is required, both from the point of view of the steganalyst and the steganographer. In this dissertation, we lay down a foundation for a thorough analysis of steganography and steganalysis and use this analysis to create practical solutions to the problems of detecting and evading detection. Detection theory, previously employed in disciplines such as communications and signal processing, provides a natural framework for the study of steganalysis, and is the approach we take. With this theory, we make statements on the theoretical detectability of modern steganography schemes, develop tools for steganalysis in a practical scenario, and design and analyze a means of escaping optimal detection. Under the commonly used assumption of an independent and identically distributed cover, we develop our detection-theoretic framework and apply it to the x steganalysis of LSB and quantization based hiding schemes. Theoretical bounds on detection not available before are derived. To further increase the accuracy of the model, we broaden the framework to include a measure of dependency and apply this expanded framework to spread spectrum and perturbed quantization hiding methods. Experiments over a diverse database of images show our steganalysis to be effective and competitive with the state-of-the-art. Finally we shift focus to evasion of optimal steganalysis and analyze a method believed to significantly reduce detectability while maintaining robustness. The expected loss of rate incurred is analytically derived and it is shown that a high volume of data can still be hidden.