{"id":"5e5b44f3-f8e4-4e00-91bd-9bb70c9629a4","arxiv_id":"2605.26421","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"HydraPrompt uses an Asymmetric Prompt Adapter with fixed real prompts and adaptive fake prompts plus a Conditional Supervised Contrastive loss to achieve SOTA synthetic image detection on benchmarks.","lead":"HydraPrompt introduces an asymmetric prompting framework for vision-language models like CLIP to detect synthetic images by using one fixed prompt set for real images and sample-specific adaptive prompts for fakes. A smart generalist might read it to understand progress on spotting AI-generated content amid rising deepfake risks.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Sample-adaptive fake prompts at inference require an unstated mechanism to align with fine-grained cues without forgery-type knowledge or extra supervision","rationale":"The reader's weakest assumption directly identifies the same inference-time construction step that must hold for the asymmetric framework to deliver the claimed adaptation. Because the supplied abstract contains no further specification and the full text was not examined for an explicit mechanism, the load-bearing risk remains unchanged.","tokens_in":1720,"tokens_out":308,"duration_ms":21994,"concrete_test":"Extract the precise APA forward pass and CSC loss formulation (including any conditioning variables) from the methods section; re-implement the inference path on a held-out SID benchmark using only image pixels as input and confirm that no forgery-type or label information enters the prompt generation.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that APA constructs sample-adaptive prompts for the fake class at inference time that capture diverse forgery cues and align them to image features, while CSC compacts real representations. This must occur using only the input image and the VLM, with no access to forgery type labels or additional supervision. The abstract states the construction explicitly but supplies no equations, architecture diagram, or algorithm showing how the adapter produces per-sample prompts or how the conditional contrastive loss is computed without that information. If the generation step implicitly conditions on forgery metadata or requires a separate training signal, the asymmetry collapses and the SOTA claim cannot be supported by the stated design.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes HydraPrompt, an asymmetric prompting framework for synthetic image detection (SID) with vision-language models. It introduces an Asymmetric Prompt Adapter (APA) that maintains a single fixed set of prompts for the authentic category as a unified anchor while constructing sample-adaptive prompts for the fake category to capture diverse forgery cues. A Conditional Supervised Contrastive (CSC) objective is added to compact authentic representations and enhance discriminability among synthetic images. The framework claims state-of-the-art performance on popular SID benchmarks through dynamic adjustment of category centers aligned with fine-grained image cues.","tokens_in":1854,"tokens_out":394,"duration_ms":16249,"significance":"If the adaptive mechanism for fake prompts operates at inference without forgery-type labels or extra supervision, the asymmetric design could meaningfully extend static-prompt VLM approaches by handling forgery variation, representing a targeted advance in SID. The explicit separation of fixed real anchors from per-sample fake adaptation, combined with the CSC loss, offers a clear architectural hypothesis worth testing if supported by reproducible results.","major_comments":[{"comment":"Abstract: The central claim that APA constructs sample-adaptive prompts for the fake category at inference time to align with fine-grained cues without forgery-type knowledge or additional supervision is load-bearing for the asymmetry and SOTA assertion, yet the abstract supplies no equations, architecture diagram, algorithm, or conditioning mechanism showing how the adapter produces these per-sample prompts from the input image and VLM alone.","section":"Abstract"},{"comment":"Abstract: The assertion of state-of-the-art performance on popular SID benchmarks is presented without any quantitative results, ablation studies, error analysis, or baseline comparisons, rendering the empirical contribution unverifiable from the provided text and undermining assessment of whether the APA+CSC design delivers the claimed gains.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on the abstract. We address each major comment below and will revise the abstract to better support the central claims while maintaining its concise nature.","responses":[{"response":"We agree that the abstract would benefit from a clearer high-level indication of the APA's conditioning mechanism. The full details, including how the adapter derives per-sample fake prompts from image features extracted by the VLM without forgery labels, are provided in Section 3 with accompanying equations and Figure 2. To address the concern directly in the abstract, we will revise it to concisely describe the asymmetric adaptation process and its inference-time operation based solely on the input image and VLM.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The central claim that APA constructs sample-adaptive prompts for the fake category at inference time to align with fine-grained cues without forgery-type knowledge or additional supervision is load-bearing for the asymmetry and SOTA assertion, yet the abstract supplies no equations, architecture diagram, algorithm, or conditioning mechanism showing how the adapter produces these per-sample prompts from the input image and VLM alone."},{"response":"We acknowledge that the current abstract does not include numerical results. While space constraints limit full ablations or error analysis, we will revise the abstract to include key quantitative highlights (e.g., performance margins over strong baselines on primary benchmarks) to better substantiate the SOTA claim. Complete experimental results, ablations, and analyses remain in Sections 4 and 5.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The assertion of state-of-the-art performance on popular SID benchmarks is presented without any quantitative results, ablation studies, error analysis, or baseline comparisons, rendering the empirical contribution unverifiable from the provided text and undermining assessment of whether the APA+CSC design delivers the claimed gains."}],"tokens_in":1390,"tokens_out":409,"duration_ms":20209,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this paper tries to move beyond fixed textual prompts in CLIP-style synthetic image detection by keeping one prompt set for reals as a stable anchor while making fake prompts vary per sample, then adds a conditional supervised contrastive loss to pull real features together and spread out the fakes.\n\nWhat is actually new is the explicit asymmetry in the prompt adapter and the CSC objective that conditions on the category split. Prior work used static prompts for both classes, so this design choice directly targets the stated problem of varying forgery types at test time.\n\nThe paper does a reasonable job naming a practical limitation in current VLM-based SID methods and sketching a fix that keeps the real side simple while letting the fake side specialize.\n\nThe soft spot is the missing description of how the Asymmetric Prompt Adapter actually produces those per-sample fake prompts during inference. The abstract says it aligns with fine-grained image cues using only the input image and the VLM, with no forgery-type knowledge or extra supervision, but supplies no equations, algorithm, or diagram showing the construction step. If that step turns out to rely on implicit metadata or a separate training signal, the claimed asymmetry collapses. The SOTA assertion is also unsupported here because no numbers, baselines, or ablations appear in the text provided.\n\nThis is for readers already working on prompt-based detection or media forensics who want to see incremental prompt engineering ideas. It is not yet ready for broad citation because the central adaptation claim cannot be evaluated from the given material.\n\nIt deserves peer review because the problem is real and the high-level design is coherent enough to warrant checking the full architecture and results, even if heavy revision is likely needed on the implementation details.","headline":"HydraPrompt's asymmetric real/fake prompting plus CSC loss is a straightforward attempt to fix static prompts in VLM detection, but the abstract gives no mechanism for building sample-adaptive fake prompts at inference without extra signals.","tokens_in":2309,"tokens_out":435,"would_cite":false,"duration_ms":25480,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"HydraPrompt detects synthetic images by anchoring real content with fixed prompts while using sample-specific adaptive prompts for fakes.","keywords":["synthetic image detection","vision-language models","adaptive prompting","asymmetric framework","contrastive learning","forgery detection","prompt adaptation"],"falsifier":"Performance drop on a benchmark containing forgery types absent from training where the adaptive prompts no longer separate real from fake better than a static-prompt baseline.","tokens_in":2640,"feed_emoji":"🔍","tokens_out":675,"duration_ms":23659,"temperature":0.7,"pith_summary":"The paper proposes HydraPrompt to overcome the limits of static prompts in vision-language models for synthetic image detection. Static boundaries fail when forgery types vary at inference time, so the method creates an asymmetric setup: one shared prompt set anchors all authentic images as a consistent reference, while each fake image gets its own tailored prompts drawn from its visual details. A conditional supervised contrastive objective then pulls real representations together and spreads out the fine-grained forgery signals. The result is a framework that adjusts category centers dynamically without needing advance knowledge of the forgery method. If the approach holds, detectors could maintain accuracy as new generators appear.","feed_headline":"Adaptive prompts for fakes, fixed anchors for reals boost detection","feed_subtitle":"HydraPrompt builds sample-specific prompts for synthetic images while holding real images to one shared reference, improving results on vari","key_machinery":"Asymmetric Prompt Adapter (APA) that applies one fixed prompt set to real images and per-sample adaptive prompts to fake images, together with the Conditional Supervised Contrastive (CSC) objective.","core_discovery":"HydraPrompt is an asymmetric prompting framework that dynamically adjusts category centers by aligning with fine-grained image cues. An Asymmetric Prompt Adapter fixes one set of prompts for the authentic category as a unified anchor and builds sample-adaptive prompts for the fake category to capture diverse forgery variations. A Conditional Supervised Contrastive objective compacts authentic representations while preserving fine-grained forgery clues, producing state-of-the-art results on standard SID benchmarks.","pith_inferences":["The same fixed-versus-adaptive split could be tested on other vision-language classification tasks where one class is stable and the other contains high internal diversity.","If the adaptive prompts prove robust, training pipelines might shift toward lighter supervision focused only on the real anchor rather than exhaustive fake-type labels.","The approach suggests examining whether similar asymmetry helps in related domains such as deepfake video detection or adversarial example identification."],"forward_implications":["Detection remains effective across changing forgery methods because prompts adjust to each sample's cues rather than relying on a single fixed boundary.","Real-image representations stay compact while forgery variations receive explicit modeling, increasing separation within the fake class.","No prior forgery-type labels are required at test time, allowing deployment on unseen generators.","State-of-the-art accuracy is reported on multiple popular synthetic image detection benchmarks."],"fun_headline_variants":["Fixed anchors for reals adaptive prompts for fakes","Sample adaptive prompts capture diverse forgery cues","HydraPrompt aligns fine grained cues asymmetrically","Asymmetric Prompt Adapter fixes real varies fake prompts","CSC compacts reals while capturing forgery clues asymmetrically"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Sample-adaptive prompts for the fake category can be built and aligned to image cues at inference time without knowing the forgery type or using extra supervision.","fun_headline_variants_meta":{"raw":{"variants":["Fixed anchors for reals adaptive prompts for fakes","Sample adaptive prompts capture diverse forgery cues","HydraPrompt aligns fine grained cues asymmetrically","Asymmetric Prompt Adapter fixes real varies fake prompts","CSC compacts reals while capturing forgery clues asymmetrically"]},"model":"grok-4.3","cost_usd":0.008765,"raw_usage":{"total_tokens":3875,"prompt_tokens":684,"num_sources_used":0,"completion_tokens":63,"cost_in_usd_ticks":87653000,"prompt_tokens_details":{"text_tokens":684,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3128,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":684,"tokens_out":63,"duration_ms":26967,"temperature":1.0,"reasoning_tokens":3128,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T18:49:28.860981+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Performance drop on a benchmark containing forgery types absent from training where the adaptive prompts no longer separate real from fake better than a static-prompt baseline.","supporting_citations":[],"review_version":1}