Traffic fingerprinting and developing defenses against them has always been an arms race between the attackers and the defenders. The rapid evolution of deep learning methods makes developing stronger traffic fingerprinting models much easier, while overhead, latency, and deployment constraints restrict the abilities of the defenses. As such, there is always the need of coming up with novel defenses against traffic fingerprinting. In this paper, we propose SMAUG, a novel CGAN-based (Conditional Generative Adversarial Network) defense to protect video streaming traffic against fingerprinting. We first assess the performance of various GANs in video streaming traffic synthesis using multiple GAN quality metrics and show that CGAN outperforms other types of GANs such as basic GANs and WGANs (Wasserstein GAN). Our proposed defense, SMAUG, uses CGANs to synthesize video traffic flows and use those synthesized flows to camouflage the original traffic that needs protection. We compare SMAUG with other state-of-the-art defenses – FPA and d*-private methods, as well as a kernel density estimation-based baseline and show that SMAUG provides better privacy with lower overhead and delay.