## 6 Detection Techniques for Deepfake Attacks
### The Performance Metrics for Deepfake Detection Algorithms  
Table 4 summarizes notable performance metrics reported in recent studies on deepfake detection [27; 28]. Instead of using accuracy and false positive rates, the Area Under the Curve (AUC) is utilized, providing a more comprehensive evaluation of the algorithms' \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Category** & **Findings** & **Challenges** \\ \hline \multirow{3}{*}{**General Cybersecurity Measures**} & Companies rely on broad cybersecurity measures from major vendors (e.g., Microsoft). Multi-factor authentication and phishing simulations are common measures. No dedicated deepfake detection tools are in use. & There is a lack of specific deepfake detections. Companies only react when threats become extensive. \\ \hline \multirow{3}{*}{**Image-, Audio-, and Video-Based Detection**} & Some companies are exploring the use of CNNs and DNNs to detect deepfake artifacts. A forensic analysis can help detect inconsistencies in media (e.g., shadows, facial movements). & High computational cost and dependency on external tools (e.g., Microsoft). \\ \hline \multirow{3}{*}{**Scalability Challenges**} & There is a need for detection systems to handle increasing occurrences of deepfakes and volumes of media. Real-time monitoring is a priority (e.g., Microsoft Teams). & Processing large-scale media efficiently without performance issues. \\ \hline \multirow{3}{*}{**Speed and Real-Time Detection**} & Companies require real-time deepfake detection, especially for live interactions (e.g., video calls). & CNNs and DNNs require significant computing power, which can cause potential delays. \\ \hline \multirow{3}{*}{**Accuracy and False Positives**} & Balancing the sensitivity to detect deepfakes while avoiding false positives is crucial. & High false positive rates can lower trust in detection systems. \\ \hline \multirow{3}{*}{**Integration with Existing Protocols**} & Companies prefer security solutions that integrate seamlessly with their current infrastructure. & Difficulty embedding advanced deepfake detection within existing cybersecurity frameworks. \\ \hline \multirow{3}{*}{**Overall Evaluation**} & The interviewed companies primarily identify deepfakes as a future risk but do not yet implement targeted detection measures. Higher-risk areas include external-facing roles (e.g., sales, executive communication). & Reliance on third-party security vendors rather than in-house solutions. \\ \hline \end{tabular}
\end{table} 
Table 3: Overview of detection techniques. performance across different threshold settings. While accuracy and the false positive rate (FPR) are common indicators for evaluating classification models, the referenced papers primarily report the AUC, as it reflects the detection performance in imbalanced datasets better. Future evaluations could incorporate these additional metrics for a more comprehensive assessment. Two datasets--Celeb-DF and FaceForensics++ (FF++)--are used for benchmarking. As shown in Table 4, several deepfake detection models achieve high AUC scores across both Celeb-DF and FF++. Models such as Xception and DCViT demonstrate a particularly strong performance, with their AUC values exceeding 97% on both datasets. Since the results for some models appeared in more than one study, the highest reported AUC values were selected to reflect the best performance achievable. This ensures that the table highlights the strongest capabilities of each model under the optimal conditions. While these results indicate strong potential for deepfake detection, deploying these models in cybersecurity requires additional work. The performance may vary depending on several factors. These include the video resolution, compression artifacts, and differences between the training data and the actual deepfake generation techniques.