Robust Sequential DeepFake Detection (2309.14991v2)

Abstract: Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However, existing methods only focus on detecting one-step facial manipulation. As the emergence of easy-accessible facial editing applications, people can easily manipulate facial components using multi-step operations in a sequential manner. This new threat requires us to detect a sequence of facial manipulations, which is vital for both detecting deepfake media and recovering original faces afterwards. Motivated by this observation, we emphasize the need and propose a novel research problem called Detecting Sequential DeepFake Manipulation (Seq-DeepFake). Unlike the existing deepfake detection task only demanding a binary label prediction, detecting Seq-DeepFake manipulation requires correctly predicting a sequential vector of facial manipulation operations. To support a large-scale investigation, we construct the first Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors. Based on this new dataset, we cast detecting Seq-DeepFake manipulation as a specific image-to-sequence task and propose a concise yet effective Seq-DeepFake Transformer (SeqFakeFormer). To better reflect real-world deepfake data distributions, we further apply various perturbations on the original Seq-DeepFake dataset and construct the more challenging Sequential DeepFake dataset with perturbations (Seq-DeepFake-P). To exploit deeper correlation between images and sequences when facing Seq-DeepFake-P, a dedicated Seq-DeepFake Transformer with Image-Sequence Reasoning (SeqFakeFormer++) is devised, which builds stronger correspondence between image-sequence pairs for more robust Seq-DeepFake detection.

• The paper introduces SeqFakeFormer to detect sequential deepfake manipulations by modeling both spatial and temporal relationships in facial image edits.
• The research presents the first Seq-DeepFake dataset, complete with perturbations, to benchmark and improve detection performance in real-world scenarios.
• The proposed transformer-based image-to-sequence framework outperforms traditional binary classifiers by capturing complex manipulation sequences with enhanced robustness.

Analyzing Robust Sequential DeepFake Detection

The proliferation of photorealistic face image generation through deep-learning-based manipulation techniques poses significant threats in the form of misinformation and forgery. The paper "Robust Sequential DeepFake Detection" tackles these concerns by addressing a specific type of deepfake manipulation: sequential deepfake manipulation. Unlike traditional methods focusing on a one-time manipulation, this research emphasizes the need to detect sequences of manipulations over the same facial image. Here, we investigate the additional complexity and trail this new paradigm introduces to the detection problem, along with the novel methodologies deployed to tackle it.

Acknowledging the existing gap in deepfake detection domains, the authors pinpoint that numerous deepfake media are constructed through sequential operations, facilitated by easy-access facial editing applications. The paper introduces Detecting Sequential DeepFake Manipulation (Seq-DeepFake) as a novel research problem, which moves beyond the binary classification of real or fake, requiring a precise capture of manipulation sequences.

The paper makes a substantial contribution by offering the first Seq-DeepFake dataset, which simulates multi-step facial manipulations. The dataset includes annotations for manipulation sequences specifically crafted for this task. To address the manifold challenges inherent in this sequential detection setup, the authors employ an image-to-sequence framework, drawing parallels to image captioning tasks. They propose a Seq-DeepFake Transformer (SeqFakeFormer), developed to meet the intricate demands of modeling facial manipulation sequences.

Two pivotal components comprise the SeqFakeFormer: Spatial Relation Extraction via an Image Encoder and Sequential Relation Modeling through a Spatially Enhanced Cross-Attention (SECA) module in the Sequence Decoder. The spatial relation extraction is achieved by a CNN that captures the fine-tuned spatial manipulation regions, transforming these features through a self-attention module. The sequential relation modeling then employs cross-attention enhanced by a dynamically generated spatial weight map that factors in manipulation-related spatial regions.

Noteworthy is the expansion of the work to provide a Seq-DeepFake dataset with perturbations (Seq-DeepFake-P), simulating real-world scenarios where images undergo various distortions. Here, a more nuanced model, SeqFakeFormer++, is introduced. This enhanced model delivers an Image-Sequence Reasoning mechanism that constructs robust image-sequence correlations, improving detection resilience against perturbations through Image-Sequence Contrastive Learning (ISC) and Image-Sequence Matching (ISM).

The paper offers rigorous benchmarks against existing deepfake methodologies, illustrating SeqFakeFormer's superiority and adaptability across different manipulation techniques and under various perturbations. These experimental outcomes entail broader implications in enhancing the robustness and precision of artificial intelligence models in detecting complex manipulative sequences in images.

While harnessing the power of transformers in capturing sequential manipulation patterns, the work sets a course for future inquiries. It propels deeper explorations into pre-encoded semantic spaces and the reconciliation of cross-path perturbations, enhancing the capacity of deep-learning frameworks in image forensics. Future strides may include refining such models' adaptability to evolving manipulation techniques and evaluating their scalability across diversified image domains.

Thus, the research steps into a pivotal domain of image forensics, observing that fostering comprehensive modeling of both spatial and temporal manipulation traces is paramount to reliable deepfake detection. Through SeqFakeFormer and SeqFakeFormer++, this paper opens new avenues in the continuous effort to safeguard digital media authenticity.