FakeSV-VLM: VLM for Short-Video Fake News

• FakeSV-VLM is a multimodal framework that fine-tunes a large vision-language model to perform binary real-versus-fake classification in short-video news.
• It employs a Progressive Mixture-of-Experts Adapter with Artifact Tokens and Alignment-driven Event Checking to capture modality-specific manipulation cues.
• Empirical results on FakeSV and FakeTT benchmarks show substantial gains over standard VLM methods, demonstrating its effectiveness for fake news detection.

Searching arXiv for the cited FakeSV-VLM and related papers. arxiv_search(query="^^^^^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^^^^^"FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter\"^^^^^^^^3 OR \3^^^^^^^^"Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos\"^^^^^^^^3 OR \3^^^^^^^^"FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms\"", max_results=^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^) arxiv_search(query="short video fake news detection FakeSV VLM FakeTT MAGIC3", max_results=^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^) FakeSV-VLM is a vision–language-model framework for detecting fake news on short video platforms. It directly fine-tunes a large VLM for binary real-versus-fake prediction while also structuring the problem around four short-video news scenarios: both video and text are real, both are fake, video real/text fake, and video fake/text real. In its published form, the framework uses InternVL^^^^3 OR \3^^^^.5-8B as the backbone and introduces Artifact Tokens, a Progressive Mixture-of-Experts Adapter (PMOE), and Alignment-driven Event Checking (ADEC) to adapt pretrained multimodal knowledge to fake short-video news detection on FakeSV and FakeTT (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

^^^^3 OR \3^^^^. Benchmark substrate and problem formulation

FakeSV-VLM operates in the setting of short-video fake news detection, where the input consists of a video and associated textual information, and the output is a binary label indicating whether the item is real or fake. In the FakeSV-VLM formulation, the input is a video PRESERVED_PLACEHOLDER_^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^, represented by keyframes, together with a textual description and event metadata PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^, and the model predicts a video-level real-versus-fake label while internally reasoning about which modality is fake. The framework is evaluated on FakeSV, a Chinese benchmark, and FakeTT, an English benchmark. In the FakeSV-VLM paper, FakeSV is described with ^^^^3 OR \3^^^^,8^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^ fake videos, ^^^^3 OR \3^^^^,8^^^^3 OR \3^^^^4 real videos, and 3,6^^^^3 OR \3^^^^4 total samples collected from Douyin and Kuaishou, with a 7^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^%/^^^^3 OR \3^^^^5%/^^^^3 OR \3^^^^5% time-based split; FakeTT is described with ^^^^3 OR \3^^^^,^^^^3 OR \3^^^^7^^^^3 OR \3^^^^ fake videos, 8^^^^3 OR \3^^^^9 real videos, and ^^^^3 OR \3^^^^,99^^^^3 OR \3^^^^ total samples from TikTok, also with a 7^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^%/^^^^3 OR \3^^^^5%/^^^^3 OR \3^^^^5% temporal split (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

The benchmark substrate is rooted in the earlier FakeSV dataset, which was introduced as a multimodal benchmark with rich social context for fake news detection on short video platforms. That original benchmark was constructed as the largest Chinese short-video fake news dataset at release, with news content, user comments, and publisher profiles. Its core binary fake-versus-real subset contains ^^^^3 OR \3^^^^,8^^^^3 OR \3^^^^7 fake videos and ^^^^3 OR \3^^^^,8^^^^3 OR \3^^^^7 real videos spanning 738 events, and the benchmark also includes debunked videos. The original dataset paper defined both event-level 5-fold cross-validation and temporal evaluation, and proposed SV-FEND as a multimodal baseline exploiting cross-modal correlations and social context (Qi et al., 2022).

Within this lineage, FakeSV-VLM narrows the operational input to video, description, and event metadata rather than the full social-context stack of the original benchmark. The main challenge it addresses is that short-video fake news can involve description forgery, content forgery, or full forgery, while realistic footage and plausible text often make unimodal judgments unreliable. The framework is therefore designed to reason over modality-level manipulation structure instead of treating the sample as a single undifferentiated multimodal object.

^^^^3 OR \3^^^^. Backbone model, inputs, and representation design

At the architectural level, FakeSV-VLM is built on InternVL^^^^3 OR \3^^^^.5-8B, described as a base VLM with a visual encoder and a 3^^^^3 OR \3^^^^-layer LLM. For each news item, the model uses 8 uniformly sampled video frames resized to PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^, together with a prompt that concatenates a system role prompt and a task prompt containing the news description, the event, and keyframe placeholders. The visual encoder $\mathcal{E}_v$ and connector $\mathcal{M}$ produce visual features

$f_v = \mathcal{M}(\mathcal{E}_v(v)),$

while tokenization of the text prompt yields

$f_t = \mathcal{T}_k(t).$

These are concatenated as

$f_c = f_v \otimes f_t.$

A distinctive element of the framework is the introduction of Artifact Tokens $A \in \mathbb{R}^{q \times d}$, with $q$ typically set to 3^^^^3 OR \3^^^^. These learnable tokens are appended to the multimodal sequence so that the early LLM layers can absorb manipulation-related cues. The combined sequence is processed by the first PRESERVED_PLACEHOLDER_^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^ LLM layers: PRESERVED_PLACEHOLDER_^^^^3 OR \3 OR \3^^^^ The paper reports that intermediate extraction works best, with the best layer for PMOE and ADEC extraction around PRESERVED_PLACEHOLDER_^^^^3 OR \3 OR \3^^^^. This placement reflects the claim that shallow layers are underdeveloped semantically, whereas deeper layers become overly abstract and lose alignment-relevant detail (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

The architecture is organized into five functional parts: the base VLM, the Progressive MoE Adapter, Manipulation-Guided Artifact Perceiving, Alignment-driven Event Checking, and final answer decoding. During inference, only the fine-tuned VLM is needed; PMOE and ADEC are used in training but do not add inference overhead. This design is central to the paper’s description of “taming” a general-purpose VLM for short-video fake news detection rather than using a raw VLM in zero-shot form.

3. Progressive Mixture-of-Experts Adapter

PMOE is the main specialization mechanism in FakeSV-VLM. It performs hierarchical reasoning over the Artifact Tokens in two stages. The first stage, Detection MoE, addresses coarse authenticity estimation: whether the sample is overall real or fake. The second stage, Attribution MoE, refines this into one of four scenarios: PRESERVED_PLACEHOLDER_^^^^3 OR \33^^^^ for both video and text real, PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^4 for video fake, PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^5 for text fake, and PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^6 for both fake.

After early-layer contextualization, the Artifact Tokens are refined by multi-query self-attention: PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^7 In Detection MoE, each token receives routing probabilities PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^8 and PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^9, and the selected expert is

PRESERVED_PLACEHOLDER_^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^

The updated token is then

PRESERVED_PLACEHOLDER_^^^^3 OR \3 OR \3^^^^

To guide this routing, the model aggregates token-level routing probabilities,

PRESERVED_PLACEHOLDER_^^^^3 OR \3 OR \3^^^^

and applies the Authenticity Probability Guidance loss

PRESERVED_PLACEHOLDER_^^^^3 OR \33^^^^

The Attribution MoE repeats this structure with four experts. Given probabilities PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^4, the routing rule becomes

PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^5

and the token update is

PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^6

This progressive arrangement is the mechanism by which FakeSV-VLM turns a binary news-verification task into an internal hierarchy of authenticity and modality-attribution decisions.

The PMOE stack is complemented by Manipulation-Guided Artifact Perceiving. MGAP first computes token weights

PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^7

then aggregates the tokens and predicts real/fake confidence scores

PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^8

The associated Artifact Classification Loss is

PRESERVED_PLACEHOLDER_^^^^3 OR \3^^^^9

and the PMOE loss is

$\mathcal{E}_v$^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^

The paper’s interpretation is that Artifact Tokens are encouraged to attend to cross-modal inconsistencies and subtle manipulation cues, while the two-stage sparse routing learns scenario-specific forgery structure rather than a single monolithic fake-news representation (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

4. Alignment-driven Event Checking, optimization, and implementation

ADEC is the explicit cross-modal consistency component of FakeSV-VLM. Its purpose is to shape the feature space so that real news yields aligned visual and textual event representations, while fake or cross-news pairs remain misaligned. Text features are processed through the first $\mathcal{E}_v$^^^^3 OR \3^^^^ LLM layers,

$\mathcal{E}_v$^^^^3 OR \3^^^^

and global event representations are obtained by average pooling: $\mathcal{E}_v$3 For a batch of $\mathcal{E}_v$4 samples, the model computes symmetric visual-to-text and text-to-visual similarities,

$\mathcal{E}_v$5

$\mathcal{E}_v$6

The match label $\mathcal{E}_v$7 is set to ^^^^3 OR \3^^^^ only when video and text belong to the same news and the news is real; it is ^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ for cross-news pairs and all fake samples. This yields

$\mathcal{E}_v$8

$\mathcal{E}_v$9

and

$\mathcal{M}$^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^

The full training objective combines binary classification, PMOE guidance, and alignment shaping: $\mathcal{M}$^^^^3 OR \3^^^^ The authors also explore an additional entropy regularizer over attribution expert probabilities,

$\mathcal{M}$^^^^3 OR \3^^^^

with

$\mathcal{M}$3

but report that this hurts performance and exclude it from the final model.

The implementation is a standard low-rank adaptation setup around InternVL^^^^3 OR \3^^^^.5-8B. Training uses 4 $\mathcal{M}$4 NVIDIA GeForce RTX 4^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ GPUs, bfloat^^^^3 OR \3^^^^6 precision, LoRA with rank 8 and alpha 3^^^^3 OR \3^^^^, batch size 4, 5 epochs per dataset, AdamW with weight decay ^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^.^^^^3 OR \3^^^^, learning-rate warmup over the first ^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3^^^^% of steps to $\mathcal{M}$5, and then cosine decay. ADEC uses $\mathcal{M}$6, and the prompt format differs between FakeSV and FakeTT to align respectively with Chinese Douyin/Kuaishou style and a neutral English presentation. The video backbone remains keyframe-based rather than explicitly temporal: frames are treated as a set of keyframes, and no explicit 3D CNN or temporal transformer is described (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

5. Empirical performance, ablations, and interpretive findings

On FakeSV, the paper reports that FakeSV-VLM reaches ACC 9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^.^^^^3 OR \3 OR \3^^^^, M-F^^^^3 OR \3^^^^ 89.97, M-P 9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^.55, and M-R 89.64. The best baseline in that study, ExMRD, reaches ACC 86.9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ and M-F^^^^3 OR \3^^^^ 86.5^^^^3 OR \3^^^^, so the reported gain is +3.3^^^^3 OR \3^^^^% ACC and +3.45% M-F^^^^3 OR \3^^^^. On FakeTT, FakeSV-VLM reaches ACC 89.3^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^, M-F^^^^3 OR \3^^^^ 87.98, M-P 87.8^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^, and M-R 88.^^^^3 OR \3^^^^7, compared with ExMRD at ACC 84.^^^^3 OR \3^^^^8 and M-F^^^^3 OR \3^^^^ 83.^^^^3 OR \33^^^^, for a reported +5.^^^^3(Wang et al., 27 Aug 2025) OR \3 OR \3^^^^% ACC and +4.85% M-F^^^^3 OR \3^^^^ improvement. The same study also reports that raw VLM baselines perform poorly, with pure VLM-based methods at approximately 66.4^^^^3 OR \3^^^^% ACC on FakeSV and 49.3^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^% on FakeTT, supporting the paper’s argument that direct but task-specific adaptation is necessary (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

The ablation results position PMOE and ADEC as complementary. PMOE, defined as Detection MoE plus Attribution MoE plus MGAP, raises performance substantially over fine-tuning the backbone alone, reaching above 9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^% ACC on FakeSV and above 88% on FakeTT even without ADEC. Detection MoE and Attribution MoE each contribute noticeable improvements individually, MGAP also improves performance, and APG is reported as important because removing it causes slight drops. ADEC alone yields partial improvements, while the full model achieves the best reported results on both benchmarks. The same ablation series finds $\mathcal{M}$7 Artifact Tokens to be optimal, identifies intermediate layers such as ^^^^3 OR \3 OR \3^^^^ and ^^^^3 OR \3^^^^6 as the most effective extraction locations, and shows that a single PMOE at layer ^^^^3 OR \3 OR \3^^^^ performs better than multiple insertions.

The paper also evaluates whether the Attribution MoE learns meaningful modality-level manipulation types despite the absence of explicit fine-grained supervision in the benchmark labels. On ^^^^3 OR \3(Wang et al., 27 Aug 2025) OR \3(Wang et al., 27 Aug 2025) OR \3^^^^ manually annotated samples with four manipulation types, the Attribution MoE reaches ACC 8^^^^3 OR \3^^^^.^^^^3(Wang et al., 27 Aug 2025) OR \3(Wang et al., 27 Aug 2025) OR \3^^^^, M-F^^^^3 OR \3^^^^ 76.^^^^3 OR \3^^^^9, M-P 74.6^^^^3 OR \3^^^^, and M-R 78.63. In a Detection MoE analysis on the FakeTT test set of ^^^^3 OR \3^^^^99 samples, the confusion matrix records TP ^^^^3 OR \3^^^^83, FN ^^^^3 OR \3^^^^7, FP ^^^^3 OR \3^^^^5, and TN 84. These analyses are used to support the claim that the model’s expert routing is not merely auxiliary regularization, but tracks interpretable manipulation scenarios.

Qualitative cases further illustrate the model’s intended operating regime. In the “White Island Volcano Eruption” example, the event is real but the video is reused from another eruption, so PMOE flags authenticity issues, Attribution MoE identifies a mismatched modality, and ADEC identifies semantic inconsistency between the event description and the visual evidence. In the “Pfizer CEO Said COVID-^^^^3 OR \3^^^^9 Vaccines Contain Microchips” example, the video itself is authentic but the claim is misinterpreted; the combined PMOE and ADEC system is reported to classify the sample as real under the dataset label. Reported failure cases include highly plausible fabricated stories paired with real footage and rare or visually unusual phenomena, such as a real meteor landing off Germany being misclassified as fake.

6. Subsequent positioning, limitations, and research significance

Later work positions FakeSV-VLM as a strong, specialized VLM detector rather than as the only viable design for short-video fake news detection. In “Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos,” FakeSV-VLM is treated as the strongest VLM-based detector for FakeSV and as the stage-^^^^3 OR \3^^^^ classifier in a two-stage system built around MAGIC$\mathcal{M}$8. That later paper reports FakeSV-VLM at ACC 89.6^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ and macro-F^^^^3 OR \3^^^^ 89.4^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ on FakeSV, while a routed MAGIC$\mathcal{M}$9+VLM system reaches ACC 9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^.93 and macro-F^^^^3 OR \3^^^^ 9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^.4^^^^3 OR \3^^^^; on FakeTT it reports FakeSV-VLM at ACC 88.9^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^ and macro-F^^^^3 OR \3^^^^ 87.6^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^, and MAGIC$f_v = \mathcal{M}(\mathcal{E}_v(v)),$^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^+VLM at ACC 89.5^^^^3 OR \3^^^^ and macro-F^^^^3 OR \3^^^^ 88.^^^^3 OR \3^^^^4. The same study frames FakeSV-VLM as a black-box VLM-based classifier used only on ambiguous cases, with the hybrid system attaining ^^^^3 OR \3^^^^8–^^^^3 OR \3^^^^7fv=M(Ev(v)),f_v = \mathcal{M}(\mathcal{E}_v(v)),^^^^3 OR \3^^^^ higher throughput and 93% VRAM savings relative to VLM-only deployment when using cached features (Tian et al., 16 Mar 2026).

This later positioning clarifies both the strengths and the boundaries of FakeSV-VLM. Its strengths are end-to-end multimodal reasoning, explicit scenario decomposition through PMOE, and strong benchmark accuracy under supervised adaptation. Its boundaries, as stated in the FakeSV-VLM paper, include scaling only to InternVL^^^^3 OR \3^^^^.5-8B due to computational constraints, the lack of fine-grained annotations in FakeSV and FakeTT, and residual vulnerability to sophisticated manipulations in which visual plausibility and semantic coherence remain high. A recurrent misconception addressed across this literature is that raw VLM knowledge alone is sufficient; both the original FakeSV-VLM results and later consistency-centric work argue instead that task-specific adaptation, modality-sensitive reasoning, and explicit alignment objectives are necessary for reliable short-video fake news detection (&&&^^^^3(Wang et al., 27 Aug 2025) OR \3^^^^&&&).

In that sense, FakeSV-VLM occupies a specific historical and technical role. It marks the transition from earlier feature-level multimodal fusion on FakeSV toward direct fine-tuning of a large VLM for short-video fake news detection, while later work treats it as a strong specialized expert that can be complemented by more interpretable consistency-centric systems. Its enduring significance lies in demonstrating that VLMs can be “tamed” for this task through specialized adapters and alignment losses, rather than being used only as zero-shot judges or as offline generators of pseudo-labels.