The　prevalence　of　check　fraud,　particularly　with　stolen　checks　sold　on　platforms　such　as　Telegram,　creates　significant　challenges　for　both　individuals　and　financial　institutions.　This　underscores　the　urgent　need　for　innovative　solutions　to　detecting　and　preventing　such　fraud　on　social　media　platforms.　While　deep　learning　techniques　show　great　promise　in　detecting　objects　and　extracting　information　from　images,　their　effectiveness　in　addressing　check　fraud　is　hindered　by　the　lack　of　comprehensive,　open-source,　large　training　datasets　specifically　for　check　information　extraction.　To　bridge　this　gap,　this　paper　introduces　＇CheckGuard,＇a　large　labeled　image-to-text　cross-modal　dataset　designed　for　check　information　extraction.　CheckGuard　comprises　over　7,000　real-world　stolen　check　image　segments　from　more　than　15　financial　institutions,　featuring　a　variety　of　check　styles　and　layouts.　These　segments　have　been　manually　labeled,　resulting　in　over　50,000　samples　across　seven　key　elements:　Drawer,　Payee,　Amount,　Date,　Drawee,　Routing　Number,　and　Check　Number.　This　dataset　supports　various　tasks　such　as　visual　question　answering　(VQA)　on　checks　and　check　image　captioning.　Our　paper　details　the　rigorous　data　collecting,　cleaning,　and　annotation　processes　that　make　CheckGuard　a　valuable　resource　for　researchers　in　check　fraud　detection,　machine　learning,　and　multimodal　large　language　models　(MLLMs).　We　not　only　benchmark　state-of-the-art　(SOTA)　methods　on　this　dataset　to　assess　their　performance　but　also　explore　potential　enhancements.　Our　application　of　parameter-efficient　fine-tuning　(PEFT)　techniques　on　the　SOTA　MLLMs　demonstrates　significant　performance　improvements,　providing　valuable　insights　and　practical　approaches　for　enhancing　model　efficacy　on　this　task.　As　an　evolving　project,　CheckGuard　will　continue　to　be　updated　with　new　data,　enhancing　its　utility　and　driving　further　advancements　in　the　field.　Our　PEFT-based　MLLM　code　is　available　at:　https://github.com/feizhao19/CheckGuard.　For　data　access,　researchers　are　required　to　contact　the　authors　directly.　©　2024　Owner/Author.