The field of synthetic content detection is experiencing rapid advancements, driven by the need to combat increasingly sophisticated deepfake and image manipulation techniques. Researchers are focusing on developing adaptive and versatile detection methods that can handle the complexity and diversity of synthetic content. Key innovations include the use of continual learning strategies to adapt models to new types of deepfakes, the integration of transformer-based architectures for universal detection across various manipulation types, and the exploration of phoneme-level feature discrepancies to enhance speech deepfake detection. Additionally, there is a growing emphasis on multimodal coherence analysis to detect inconsistencies in talking face generation. These developments not only improve the accuracy and robustness of detection methods but also broaden their applicability to a wider range of synthetic content scenarios. Notably, the introduction of new datasets and frameworks is filling critical gaps in the research, enabling more comprehensive evaluations and advancements in the field. Furthermore, advancements in adversarial techniques and robust models are shaping the direction of research, with a focus on creating universal and transferable solutions that enhance robustness against adversarial attacks while ensuring imperceptibility and efficiency. Innovations in watermarking and image protection are also prominent, with a strong emphasis on balancing robustness, fidelity, and computational efficiency. The integration of physical-world considerations into adversarial attacks and defenses is emerging as a critical area, highlighting the need for practical solutions that address real-world vulnerabilities in surveillance and image processing systems.